Precision medicine for treating and preventing suicidality

ABSTRACT

The present disclosure relates generally to discovery of novel compounds involved in the treatment and prevention of suicidality by bioinformatics drug repurposing using novel genes expression biomarkers involved in suicidality. Disclosed are methods for assessing severity, determining future risk, matching with a drug treatment, and measuring response to treatment, for suicidality. Also disclosed are new methods of use for drugs and natural compounds repurposed for use in preventing and treating suicidality. These methods include computer-assisted methods analyzing the expression of panels of genes, clinical measures, and drug databases. Detailed herein are methods using a universal approach, in everybody, as well as personalized approaches by gender, and by diagnosis. The discovery describes compounds for use in everybody (universal), as well as personalized by gender (males, females), diagnosis (bipolar, depression), gender and diagnosis combined (male bipolar, male depression), male PTSD, male SZ/SZA), and subtypes of suicidality (high anxiety, low mood, combined (affective), and high psychosis (non-affective). Also disclosed are methods for identifying which subjects should be receiving which treatment, using genes expression biomarkers for patient stratification and measuring response to treatment. The disclosure also relates to algorithms, universal and personalized by gender and diagnosis. The algorithms combine biomarkers as well as clinical measures for suicidality and for mental state, in order to identify subjects who are at risk of committing suicide, as well as to track responses to treatments. The disclosure further relates to determining subtypes of suicidality. Such subtypes may delineate groups of individuals that are more homogenous in terms of biology, behavior, and response to treatment.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part of and claims priority to PCT Application serial number PCT/US2018/032540, filed May 14, 2018, which claims priority to U.S. Provisional Application No. 62/505,197 filed on May 12, 2017, the contents of both of which are incorporated herein by reference in their entirety.

STATEMENT OF GOVERNMENT SUPPORT

This invention was made with government support under OD007363 awarded by the National Institutes of Health and 2IO1CX000139 merit award by the Veterans Administration. The government has certain rights in the invention.

BACKGROUND OF THE DISCLOSURE

Suicide is a leading cause of death in psychiatric patients, and in society at large. Particularly, suicide accounts for one million deaths worldwide each year. Worldwide, one person dies every 40 seconds through suicide, a potentially preventable cause of death. Further, although women have a lower rate of suicide completion as compared to men, due in part to the less-violent methods used, women have a higher rate of suicide attempts. A limiting step in the ability to intervene is the lack of objective, reliable predictors. One cannot just ask individuals if they are suicidal, as the desire to not be stopped or future impulsive changes of mind may make their self-report of feelings, thoughts and plans unreliable.

There are currently no objective tools to assess and track changes in suicidal risk without asking the subjects directly. Such tools, however, could prove substantially advantageous as the subjects at risk often choose not to share their suicidal ideation or intent with others, for fear of stigma, hospitalization, or that their plans will be thwarted. The ability to assess and track changes in suicidal risk without asking a subject directly would further allow for intervening prior to suicide attempt and suicide completion by the subject.

SUMMARY

Based on the foregoing, objective and precise identification of individuals at risk, ways of monitoring response to treatments, and novel preventive therapeutics need to be discovered, employed, and widely deployed. Particularly, objective and quantitative markers would permit better and more precise assessment, tracking, and prediction of suicidal risk, which would enable preventive therapeutic interventions. Accordingly, the present disclosure is directed to identifying universal predictors, and in some embodiments, personalized predictors for suicidality. The present disclosure is generally directed at methods for assessing suicidality and early identification of risk for future suicidality, as well as methods for matching patients and drugs for prevention and mitigation of suicidality, and for monitoring response to treatment. Further, the present disclosure describes new methods of use for drugs and natural compounds repurposed for treating suicidality. All the above-mentioned methods are computer-assisted methods analyzing the expression of panels of genes, clinical measures, and drug databases. A universal approach in everybody, as well as a personalized approach by gender, and by diagnosis, are disclosed.

The present disclosure relates generally to compounds for mitigating suicidality. Particularly, novel drugs and natural compounds for treating and preventing suicidality (e.g., suicide ideation and actions, future hospitalization due to suicidality, and suicide completion) have now been identified through bioinformatics drug repurposing methods using novel gene expression biomarkers. The disclosure describes compounds for use in everybody (universal), as well as personalized by gender (males, females), diagnosis (bipolar, depression), and gender and diagnosis combined (male bipolar, male depression). Further, the present disclosure relates to gene expression biomarkers and their use for deciding in a particular person which drug or natural compound to use (precision medicine) for treating and preventing suicidality (e.g., suicide ideation and actions, future hospitalization due to suicidality, and suicide completion), as well as for tracking response to the drug or natural compound (pharmacogenomics). More particularly, the present disclosure relates to an algorithm composed of clinical measures and biomarkers for identifying subjects who are at risk of committing suicide, as well as for monitoring response to treatment. In some embodiments, the biomarkers used herein have been found to be more universal in nature, working across psychiatric diagnoses and genders. Such biomarkers may reflect and/or be a proxy for the core biology of suicide. In other embodiments, the present disclosure relates to biomarkers identified using a personalized approach; that is, by psychiatric diagnosis and/or gender, with a focus on bipolar males, the highest risk group. Such a personalized approach may be more sensitive to gender differences and to the impact of psychiatric co-morbidities and medications.

The present disclosure further relates to determining subtypes of suicidality using an app (SASS), based on mental state at the time of high suicidal ideation, and identified four subtypes: high anxiety, low mood, combined, and non-affective (psychotic). Such subtypes may delineate groups of individuals that are more homogenous in terms of biology and behavior.

The present disclosure further relates to a checklist of socio-demographic and psychological factors that influence the likelihood of becoming suicidal (CFI-S), with contributions from six domains (life events, mental health, physical health, environmental factors, cultural factors, and addictions). It can provide a likelihood score for an individual attempting that behavior (suicide) in the future. The items that are positive on the checklist can have differences in importance embodied as weight coefficients, based on specificity for suicide (Table 1), and based on empirical data, such as rank order in predictive datasets (FIGS. 4A & 4B). They also vary from individual to individual. As such, there is an individualized profile that can be affected by targeted interventions to prevent that behavior (suicide).

TABLE 1 Convergent Functional Information for Suicidality (CFI-S 30) Scale Items are scored 1 for Yes, 0 for No. Total Score has a maximum possible of 30. Final Score is Total Score divided by number of items that were scored (as for some items information might not be available (NA) so they are not scored), and multiplied by 100. Weights for sensitivity/ Importance Type to behavior Increased Weights for 3 is most Reasons specificity2 important, (IR) is Specific for 2 intermediate, Decreased Suicidality, 1 less Barriers 1 is non- Weighted Items Yes = 1 No = 0 NA Domain important (DB) specific Score 1. Psychiatric illness Mental x2 IR x1 diagnosed and treated Health 2. With poor Mental x2 DB  1 treatment compliance Health 3. Family history of Mental x2 IR x2 suicide in blood relatives Health 4. Personally Cultural x2 DB x2 knowing somebody who Factors committed suicide 5. History of abuse Life x3 IR x1 growing up: physical, Satisfaction sexual, emotional, neglect 6. Acute/severe Physical x1 IR x1 medical illness, including Health acute pain (“I just can't stand this pain anymore.”) (within last 3 months) 7. Acute stress: Environmental x1 IR x1 Losses, grief (within last Stress 3 months) 8. Chronic stress: Environmental x1 IR x1 perceived uselessness, Stress not feeling needed, burden to extended kin. 9. History of Mental x2 IR x1 excessive introversion, Health conscientiousness (including planned suicide attempts) 10. Dissatisfaction Life x3 IR x1 with life at this moment Satisfaction in time 11. Lack of hope for Life x3 IR x1 the future Satisfaction 12. Current substance Addictions x3 DB x1 abuse 13. Past history of Life x3 DB x2 suicidal acts/gestures Satisfaction 14. Lack of religious Cultural x2 DB x1 beliefs Factors 15. Acute stress: Environmental x1 IR x1 Rejection (within last 3 Stress months) 16. Chronic stress: Environmental x1 DB x1 lack of positive Stress relationships, social isolation 17. History of Mental x2 DB x1 excessive extroversion Health and impulsive behaviors (including rage, anger, physical fights) 18. Lack of coping Mental x2 DB x1 skills when faced with Health stress (cracks under pressure) 19. Lack of children. If Life x3 DB x1 has children, not in touch/ Satisfaction not helping take care of them. 20. History of Mental x2 IR x2 command hallucinations Health of self-directed violence 21. Age: Older >60 or Age x1 IR x1 Younger <25 22. Gender: Male or Gender  1 DB  1 Transgender 23. Persistent reduced Mental x2 IR x1 (<5 hrs/night), excessive Health (>11 hrs/night) or fragmented sleep (within the last 3 months) 24. History of head Physical x1 DB x1 trauma/traumatic brain Health injury 25. Owns/has easy Cultural x2 DB x2 access to guns or to Factors multiple medications 26. History of Life x3 IR x1 exposure to trauma as an Satisfaction/ adult: combat, accidents, Environmental violence, rape Stress 27. Is an artist or Cultural x2 DB x1 entertainer, or works in Factors the healthcare field as a provider of clinical care 28. History of revenge Mental x2 DB x1 behaviors Health 29. History of feeling Mental x2 DB x1 very guilty Health 30. Does not easily Cultural x2 DB x1 confide or seek help from Factors others Total score = (Sum of Weighted score/Number of items scored) × 100

Biomarkers underlying propensity to behaviors can also be identified, as described in the present disclosure. They can be viewed as a checklist of biological measures. Again, the items/biomarkers that are positive/changed in levels on the checklist can have different weights of importance embodied as weight coefficients, based on specificity for suicide as reflected in a convergent functional genomics (CFG) score obtained during their discovery, prioritization and validation, (Table 1), and also based on other empirical data, such as strength in predictive datasets (FIGS. 2 and 3A-3D). They also vary from individual to individual. There is an individualized profile that can be affected by targeted interventions, such as matched nutraceuticals and medications, as described in our invention.

Besides the checklists of factors that influence behavior (such as CFI-S in the case of suicide), and the checklist of biomarkers that indicate propensity to a behavior, such as panels of predictive biomarkers, the state of mind of an individual is a major factor influencing whether a behavior will happen or not. So a checklist of measures of the mind domains (anxiety and mood (for example measured with SASS), psychosis (for example measured with PANSS Positive Scale), and a direct assessment of the severity of suicidal ideation (for example measured with the suicide item in HAMD (HAMD-SI), would be informative to include in the overall algorithm to predict suicidality, and as targets for intervention to facilitate or prevent behaviors.

BRIEF DESCRIPTION OF THE DISCLOSURE

The present disclosure is generally directed at methods for assessing suicidality and early identification of risk for future suicidality, as well as methods for matching patients and drugs for prevention and mitigation of suicidality, and for monitoring response to treatment. The present disclosure is further related to drugs for mitigating suicidality in subjects. Particular drugs have been found that can mitigate suicidality in subjects universally; that is, drugs that can be used for mitigating suicidality across psychiatric diagnoses, genders and subtypes of suicidality. Some drugs, however, have been found that can be used more effectively for mitigating suicidality dependent on gender, psychiatric diagnoses, subtypes and combinations thereof.

Additionally, the present disclosure relates to biomarkers and their use for predicting a subject's risk of suicidality. In some embodiments, the biomarkers used herein have been found to be more universal in nature, working across psychiatric diagnoses, genders and subtypes. In other embodiments, the present disclosure relates to biomarkers identified using a personalized approach; that is, by psychiatric diagnosis, gender and subtype.

The present disclosure further relates to determining subtypes of suicidality based on mental state at the time of high suicidal ideation, and identified four subtypes: high anxiety, low mood, combined, and psychotic (non-affective) such to delineate groups of individuals that are more homogenous in terms of biology and behavior.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawings will be provided by the Office upon request and payment of the necessary fee.

The disclosure will be better understood, and features, aspects and advantages other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such detailed description makes reference to the following drawings, wherein:

FIGS. 1A-1G depict Discovery, Prioritization and Validation methodology used in the Examples. (FIG. 1A) Cohorts used in the Examples, depicting flow of discovery, prioritization, and validation, and testing of biomarkers from each step. (FIG. 1B) Discovery cohort longitudinal within-participant analysis. Phchp ### is study ID for each participant. V # denotes visit number (1, 2, 3, 4, 5, or 6). (FIG. 1C) Discovery of subtypes of suicidality based on high suicidal ideation visits in the discovery cohort. Subjects were clustered using measures of mood and anxiety (SASS), as well as psychosis (PANS S Positive). (FIG. 1D) Differential gene expression in the Discovery cohort-number of genes identified with DE and AP methods with an internal score of 1 and above. Underlined-increased in expression in High SI, no underline—decreased in expression in High SI. At the discovery step probesets were identified based on their score for tracking suicidal ideation with a maximum of internal points of 4 (33% (1 pt), 50% (2 pt) and 80% (4 pt)). (FIG. 1E) Prioritization with CFG for prior evidence of involvement in suicide. In the prioritization step probesets were converted to their associated genes using Affymetrix annotation and GeneCards. Genes were prioritized and scored using CFG for Suicide evidence with a maximum of 8 external points. Genes scoring at least 4 points out of a maximum possible of 12 total internal and external score point were carried to the validation step. (FIG. 1F) Validation in an independent suicide completers cohort from the coroner's office. In the validation step biomarkers were assessed for stepwise change from the discovery groups of participants with no SI, to high SI, to suicide completion, using ANOVA. Stringent Bonferroni correction is calculated for the total number of probesets analyzed. (FIG. 1G) Discovery, Prioritization and Validation scores for the cohorts in the Examples.

FIG. 2 depicts the best universal individual biomarkers for predicting suicidality out of the top dozen and Bonferroni validated biomarkers.

FIGS. 3A-3D depict the best biomarkers predicting suicidality as found in the Examples. Best individual biomarkers out of top dozen and Bonferroni validated. FIG. 3A is a circos plot depicting the best individual biomarker predictions for suicidal ideation state in the independent cohort (across all subjects, in subtypes, and personalized by gender and diagnosis), using universal biomarkers. FIG. 3B is a circos plot depicting the best individual biomarker predictions for future hospitalizations for suicidality in the first year following testing in the independent cohort (across all subjects, in subtypes, and personalized by gender and diagnosis), using universal biomarkers. FIG. 3C is a circos plot depicting the best individual biomarker predictions for suicidal ideation state in the independent male bipolar sub-cohort, using universal biomarkers and male bipolar biomarkers. FIG. 3D is a circos plot depicting the best individual biomarker predictions for future hospitalizations for suicidality in the first year following testing in the independent male bipolar sub-cohort, using universal biomarkers and male bipolar biomarkers. The circumference bands represent and are proportional to the number of participants in each cohort. The ribbons represent and are proportional to the AUC of the predictions. Table underneath the figures displays the actual numerical results. Only biomarkers whose AUC p-values are at least nominally significant are shown.

FIG. 3E The predictive ability of the biomarkers from FIGS. 3A-3D, shown in numerical fashion (AUC, p-value), in all (universal), by subtypes, and by gender and diagnosis.

FIGS. 4A & 4B depict Convergent Functional Information for Suicide (CFI-S) Testing. Testing in a large cohort that combines the discovery and test cohorts used for biomarker work. CFI-S was developed independently of any data from the Examples, by compiling known socio-demographic and clinical risk factors for suicide. It is composed of a short version with 22 items, and a longer version with 30 items (Table 1), that assess the influence of mental health factors, as well as of life satisfaction, physical health, environmental stress, addictions, and cultural factors known to influence suicidal behavior, as well as two demographic factors, age and gender. FIG. 4A depicts prediction of high suicidal ideation (HAMD SI>=2). FIG. 4B depicts prediction of future hospitalizations due to suicidality within one year of follow up. Table under FIG. 4A depicts individual items and their ability to differentiate between No SI and High SI. Table under FIG. 4B depicts participants with and without future hospitalizations due to suicidality.

FIGS. 5A-5C depict predicting suicidality using a broad-spectrum predictor (UP-Suicide), combining phenomic measures and the top dozen biomarkers. FIG. 5D-5E depict broad-spectrum predictor (UP-Suicide), combining phenomic measures and the top dozen biomarkers in a single research participant (phchp328). FIG. 5A depicts the UP-Suicide model. FIG. 5B depicts UP-Suicide predicting suicidal ideation in the independent test cohort, and predicting future hospitalizations due to suicidality in the first year following testing. UP-Suicide is composed of the top increased and decreased biomarkers from each step of discovery, prioritization, and validation, for a total of 12, along with CFI-S scores and SASS (Mood and Anxiety scores). n=number of testing visits. Top left Receiver operating curve identifying participants with suicidal ideation against participants with No SI or intermediate SI. Top right Y axis contains the average UP-Suicide scores with standard error of mean for no SI, intermediate SI, and high SI. Scatter plot depicting HAMD-SI score on the Y-axis and UP-Suicide score on the X axis with linear trend line. The table below FIG. 5B top left receiver operating curve and top right summarizes descriptive statistics. Bottom left Receiver operating curve identifying participants with future hospitalizations due to suicidality against participants without future hospitalizations due to suicidality. Top right Y axis contains the average UP-Suicide scores with standard error of mean for no future hospitalizations due to suicidality and participants with future hospitalizations due to suicidality. Scatter plot depicting frequency of future hospitalizations due to suicidality on the Y-axis and UP-Suicide score on the X axis with linear trend line. The table below FIG. 5B bottom left receiver operating curve and bottom right summarizes descriptive statistics. FIG. 5C is a dimensional view of risk stratification using clinical information measures, and example of two high risk participants. A tri-dimensional scatter plot was created using Partek. Tri-dimensional 95% confidence intervals were inserted as ellipsoids, color coded blue and red, for No SI and High SI, respectively. Euclidian D (distance from origin) is depicted for the 2 subjects, as indicated by the arrows. Percentiles for scores on top predictors in all the subjects' visits in this Example are depicted in the table underneath the plot. Participant phchp158 was a divorced African American male in his late 20s with a long history of schizoaffective disorder, bipolar type, and Cannabis abuse. He was tested once (v1) while hospitalized for a suicide attempt by hanging. In the five years following testing, he had two additional hospitalizations for suicidality: one for suicidal ideation, one for attempt by overdose. He also had two hospitalizations for psychosis exacerbation without suicidality during this time span. Moved out of state, lost to follow-up since December 2015. Participant phchp328 (FIGS. 5D and 5E) was a Caucasian female in her late 30s with a long history of depression, PTSD, borderline personality disorder, and polysubstance abuse/dependence. She was first tested while in-patient for suicidal ideation. Over the next year, she subsequently had six psychiatric hospitalizations for suicidality: five due to suicidal ideation and one due to a suicidal attempt by overdose. She also had one hospitalization for opioid withdrawal and depression during this time span. She committed suicide by overdose with pills, leaving behind a suicide note addressed to her mother. Her UP-Suicide score at Visit 1, composed of the panel of top dozen biomarkers (BioM12) scores and phenomic measures scores (CFI-S, SASS), was at the 100% of the scores of all the psychiatric participant visits in the Example. Of note, that testing was conducted during an in-patient hospitalization due to suicidal ideation. While her scores improved at subsequent outpatient testing visits (Visits 2 and 3), this high watermark score indicated her high risk. After the last testing visit for the Example, she had four subsequent psychiatric hospitalizations: three due to suicidal ideation, one for opioid withdrawal/detox (the last one), ending 2 weeks before date of committing suicide (T). FIG. 5D provides percentiles for scores on top predictors in the subjects' visits. FIG. 5E is a dimensional view of risk stratification using clinical information measures, and example of two high risk participants. A tri-dimensional scatter plot was created using Partek. Tri-dimensional 95% confidence intervals were inserted as ellipsoids, color coded blue and red, for No SI and High SI, respectively.

FIG. 6 depicts UP-Suicide across all, by subtypes, and personalized by gender/diagnosis. UP-Suicide is composed of the panel of the top dozen universal biomarkers, CFI-S, and SASS (Anxiety, Mood). Plot depicts Area Under the Curve (AUC) for the UP-Suicide predicting suicidal ideation and hospitalizations within the first year in all participants, as well as separately in subtypes, and by gender and diagnosis (Gender/Dx). Two asterisks indicate the comparison survived Bonferroni correction for all the multiple comparisons depicted. A single asterisk indicates nominal significance of p<0.05. Bold outline indicates that the UP-Suicide was synergistic to its components, i.e., performed better than the gene expression biomarkers or phenomic data individually. The table below contains descriptive statistics for all participants together, as well as separately by subtypes, and by gender/dx. Bold indicates the measure survived Bonferroni correction for all the multiple comparisons depicted. Pearson correlation data is also shown in the suicidal ideation test cohort for HAMD-SI vs. UP-Suicide, as well as Pearson correlation data in the hospitalization test cohort for frequency of hospitalizations for suicidality in the first year, and for frequency of hospitalizations for suicidality in all future available follow-up intervals (which varies among participants, from 0.40 to 10.42 years).

FIG. 7 depicts universal biomarkers—Convergent Functional Evidence for Involvement in Suicidality. Top dozen and Bonferroni validated biomarkers. Post-hoc summation of all the evidence form discovery, validation, prioritization and testing, along with evidence for being a target of drugs and for involvement in other psychiatric disorders. This prioritization highlights for future studies biomarkers that may have broad applicability in the field, for diagnostics and therapeutics.

FIG. 8 depicts a STRING analysis depicting interactions between universal biomarkers. Top Dozen and Bonferroni combined lists.

FIG. 9 depicts Male Bipolar Biomarkers—Convergent Functional Evidence for Involvement in Suicidality. Top Dozen and Bonferroni biomarkers. Post-hoc summation of all the evidence form discovery, validation, prioritization and testing, along with evidence for involvement in other psychiatric disorders and for being a target of drugs. This prioritization highlights, for future studies, biomarkers that may have broad applicability in the field, for diagnostics and therapeutics. BP—bipolar, MDD—major depressive disorder, SZ—schizophrenia, PTSD—post-traumatic stress disorder, ASD—autism spectrum disorder;

FIG. 10 is a schematic diagram depicting top blood biomarkers for suicidality (BioM50) in accordance with embodiments of the present disclosure;

FIGS. 11A-11C depict the best Single Biomarkers Predictors for Suicidality State, and for Trait (Future Hospitalizations for Suicidality) from top candidate biomarkers from each of the Steps 1-3 (Discovery, Prioritization, Validation-Bold). FIG. 11A depicts state predictions-high suicidal ideation (HAMDSI>=2). FIG. 11B depicts trait predictions-first year hospitalizations for suicidality. FIG. 11C depicts trait predictions-all future years hospitalizations for suicidality. Bar graphs show the best predictive biomarkers in each group. * Nominally significant p<0.05. The tables underneath FIGS. 11A-11C display the actual number of biomarkers for each group whose ROC AUC p-values (FIGS. 11A-B) and Cox Odds Ratio p-values (FIG. 11C) are at least nominally significant. Some gender and diagnosis group are missing from the graph as they did not have any significant biomarkers. Cross-sectional is based on levels at one visit. Longitudinal is computed based on levels at multiple visits (integrates levels at most recent visit, maximum levels, slope into most recent visit, and maximum slope). Dividing lines represent the cutoffs for a test performing at chance levels (white), and at the same level as the best biomarkers for all subjects in cross-sectional (gray) and longitudinal (black) based predictions. All biomarkers performed better than chance. Biomarkers performed better when personalized by gender and diagnosis;

FIG. 12 is a schematic diagram depicting the matching of patients to drugs, the pharmacogenomics for suicidality. FIG. 12 depicts the top biomarkers, from the BioM 50 panel, with modulation capabilities by existing drugs in the opposite direction to suicidality. Such biomarkers can be used to target treatments to different patients, and to measure response to that treatment. The higher the proportion/percentile of biomarkers for a certain drug/class, the more indicated that drug would be for treatment. When biomarkers for multiple different drug/classes are changed in an individual, a prioritization based on the proportion/percentile of biomarkers for each class can be used to choose the drug or combination of drugs (targeted rational polypharmacy);

FIG. 13 depicts a STRING analysis depicting interactions between Top CFE BioM 50 Biomarkers (n=46 top genes, 50 probesets). The links between nodes depict various types of evidence of interaction (see (https://string-db.org). The STRING interaction analysis revealed at least 3 biological networks (centered on NR3C1, PSMB4, and SOD2), which represent biomarkers and networks/pathways which can be targets for new drug development;

FIG. 14 depicts a schematic diagram of generating risk score and personalized medication options based on a panel of biomarkers, according to embodiments of the disclosed methods;

FIG. 15 depicts a representation of a report providing a risk score and personalized treatment options, according to embodiments of the disclosed methods.

DETAILED DESCRIPTION

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the disclosure belongs. Although any methods and materials similar to or equivalent to those described herein can be used in the practice or testing of the present disclosure, the preferred methods and materials are described below.

The present disclosure is generally directed at methods for assessing suicidality and early identification of risk for future suicidality, as well as methods for matching patients and drugs for prevention and mitigation of suicidality, and for monitoring response to treatment. The methods may further include the generation of a report providing a risk score and/or personalized treatment options. Further, the present disclosure generally is directed to drugs for mitigating suicidality in subjects. Particular drugs have been found that can mitigate suicidality in subjects universally; that is, drugs that can be used for mitigating suicidality across psychiatric diagnoses and genders. Some drugs, however, have been found that can be used more effectively for mitigating suicidality dependent on gender, psychiatric diagnoses, and combinations thereof.

In additional embodiments, the present disclosure is directed to blood gene expression biomarkers that are more universal in nature; that is, blood biomarkers that can be used for predicting suicidality across psychiatric diagnoses and genders. Accordingly, a longitudinal within-participant design and large cohorts were used.

Additionally, subtypes of suicidality were identified based on mental state (anxiety, mood, psychosis) at the time of high suicidal ideation.

Furthermore, the predictive ability of the biomarkers discovered were examined, in a completely independent cohort, in all the participants in it, as well as divided by subtypes, and personalized by gender and diagnosis.

The top biomarkers were combined with scores from a clinical information measure of suicide risk (CFI-S), as well as anxiety and mood (SASS), to obtain a broader spectrum predictor (UP-Suicide) that puts the biomarkers in the context of the person and his/her mental state. This list was then leveraged for therapeutics and drug discovery purposes to see if some of the biomarkers identified could be modulated by existing compounds used to treat suicidality, and also to conduct bioinformatics drug repurposing analyses to discover new drugs and natural compounds that may be useful for treating suicidality.

As disclosed herein, “patient psychiatric information” may include mood information, anxiety information, psychosis information and other psychiatric symptom information and combinations thereof.

As used herein, “predicting suicidality in a subject” is used herein to indicate in advance that a subject will attempt suicide and/or complete suicide.

As known by those skilled in the art, “suicidal ideation” refers to thoughts, feelings, intent, external actions and behaviors about completing suicide. Suicidal ideation can vary from fleeting thoughts to unsuccessful attempts. In some embodiments, the reference expression level of a biomarker can be obtained for a subject who has no suicidal ideation at the time the sample is obtained from the subject, but who later exhibits suicide ideation. As used herein, “suicidality” includes both suicide ideation and suicidal acts.

As used herein, “a reference expression level of a biomarker” refers to the expression level of a biomarker established for a subject with no suicidal ideation, expression level of a biomarker in a normal/healthy subject with no suicidal ideation as determined by one skilled in the art using established methods as described herein, and/or a known expression level of a biomarker obtained from literature. The reference expression level of the biomarker can further refer to the expression level of the biomarker established for a high suicide risk subject, including a population of high suicide risk subjects. The reference expression level of the biomarker can also refer to the expression level of the biomarker established for a low suicide risk subject, including a population of low suicide risk subjects. The reference expression level of the biomarker can also refer to the expression level of the biomarker established for any combination of subjects such as a subject with no suicidal ideation, expression level of the biomarker in a normal/healthy subject with no suicidal ideation, expression level of the biomarker for a subject who has no suicidal ideation at the time the sample is obtained from the subject, but who later exhibits suicide ideation, expression level of the biomarker as established for a high suicide risk subject, including a population of high suicide risk subjects, and expression level of the biomarker can also refer to the expression level of the biomarker established for a low suicide risk subject, including a population of low suicide risk subjects. The reference expression level of the biomarker can also refer to the expression level of the biomarker obtained from the subject to which the method is applied. As such, the change within a subject from visit to visit can indicate an increased or decreased risk for suicide. For example, a plurality of expression levels of a biomarker can be obtained from a plurality of samples obtained from the same subject and used to identify differences between the plurality of expression levels in each sample. Thus, in some embodiments, two or more samples obtained from the same subject can provide an expression level(s) of a blood biomarker and a reference expression level(s) of the blood biomarker.

As used herein, “expression level of a biomarker” refers to the process by which a gene product is synthesized from a gene encoding the biomarker as known by those skilled in the art. The gene product can be, for example, RNA (ribonucleic acid) and protein. Expression level can be quantitatively measured by methods known by those skilled in the art such as, for example, northern blotting, amplification, polymerase chain reaction, microarray analysis, tag-based technologies (e.g., serial analysis of gene expression and next generation sequencing such as whole transcriptome shotgun sequencing or RNA-Seq), Western blotting, enzyme linked immunosorbent assay (ELISA), and combinations thereof.

As used herein, a “difference” in the expression level of the biomarker refers to an increase or a decrease in the expression of a blood biomarker when analyzed against a reference expression level of the biomarker. In some embodiments, the “difference” refers to an increase or a decrease by about 1.2-fold or greater in the expression level of the biomarker as identified between a sample obtained from the subject and the reference expression level of the biomarker. In one embodiment, the difference in expression level is an increase or decrease by about 1.2 fold. As used herein “a risk for suicide” can refer to an increased (greater) risk that a subject will attempt to commit suicide and/or complete suicide. For example, depending on the biomarker(s) selected, the difference in the expression level of the biomarker(s) can indicate an increased (greater) risk that a subject will attempt to commit suicide and/or complete suicide. Conversely, depending on the biomarker(s) selected, the difference in the expression level of the biomarker(s) can indicate a decreased (lower) risk that a subject will attempt to commit suicide and/or complete suicide.

In accordance with the present disclosure, biomarkers useful for objectively predicting, mitigating, and/or preventing suicidality in subjects have been discovered. In one aspect, the present disclosure is directed to a universal method for predicting suicidality in a subject; that is, a method for predicting suicidality across all psychiatric diagnoses and for either gender. The method includes obtaining a reference expression level of a blood biomarker; and determining an expression level of the blood biomarker in a sample obtained from the subject. A change in the expression level of the blood biomarker in the sample obtained from the subject as compared to the reference expression level indicates suicidality. In some embodiments, the methods further include obtaining clinical risk factor information and clinical scale data such as for anxiety, mood and/or psychosis from the subject in addition to obtaining blood biomarker expression level in a sample obtained from the subject.

In one embodiment, the expression level of the blood biomarker in the sample obtained from the subject is increased as compared to the reference expression level of the biomarker. It has been found that an increase in the expression level of particular blood biomarkers in the sample obtained from the subject as compared to the reference expression level of the biomarker indicates a risk for suicide. Suitable biomarkers that indicate a risk for suicide when the expression level increases can be, for example, one or more biomarkers as listed in Tables 3A-3G and combinations thereof.

In another embodiment, the expression level of the blood biomarker in the sample obtained from the subject is decreased as compared to the reference expression level of the biomarker. Suitable biomarkers that indicate a risk for suicide when the expression level decreases as compared to the reference expression level have been found to include, for example, one or more biomarkers as listed in Tables 3A-3G and combinations thereof.

Particularly suitable subjects are humans. Suitable subjects can also be experimental animals such as, for example, monkeys and rodents, that display a behavioral phenotype associated with suicide, for example, a mood disorder or psychosis. In one particular aspect, the subject is a female human. In another particular aspect, the subject is a male human, and in another particular aspect, the subject is a male bipolar human. In yet another particular aspect, the subject is a male depressed human.

A particularly suitable sample for which the expression level of a biomarker is determined can be, for example, blood, including whole blood, serum, plasma, leukocytes, and megakaryocytes.

The method can further include assessing mood, anxiety, psychosis and other like psychiatric symptoms, and combinations thereof in the subject using questionnaires and/or a computer-implemented method for assessing mood, anxiety, psychosis, other like psychiatric symptoms, and combinations thereof. In one aspect, the method is implemented using a first computer device coupled to a memory device, the method comprising: receiving mood information, anxiety information, psychosis information and combinations thereof into the first computer device; storing, by the first computer device, the mood information, anxiety information, psychosis information and combinations thereof in the memory device; computing, by the first computer device, of the mood information, anxiety information, psychosis and combinations thereof, a score that can be used to predict suicidality; presenting, by the first computer device, in visual form the mood information, anxiety information, psychosis information and combinations thereof to a second computer device; receiving a request from the second computer device for access to the mood information, anxiety information, psychosis information and combinations thereof; and transmitting, by the first computer device, the mood information, anxiety information, psychosis information and combinations thereof to the second computer device to assess mood, anxiety, psychosis and combinations thereof in the subject. Suitable mood and anxiety information is described herein in more detail below.

The method can further include assessing socio-demographic/psychological suicidal risk factors in the subject using a computer-implemented method for assessing socio-demographic/psychological suicidal risk factors in the subject, the method implemented using a first computer device coupled to a memory device, the method comprising: receiving socio-demographic/psychological suicidal risk factor information into the first computer device; storing, by the first computer device, the socio-demographic/psychological suicidal risk factor information in the memory device; presenting, by the first computer device, in visual form the socio-demographic/psychological suicidal risk factor information to a second computer device; receiving a request from the second computer device for access to socio-demographic/psychological suicidal risk factor information; and transmitting, by the first computer device, the socio-demographic/psychological suicidal risk factor information to the second computer device to assess the socio-demographic/psychological suicidal risk factors in the subject. Suitable socio-demographic/psychological suicidal risk factors are described herein in more detail below.

In accordance with embodiments of the present disclosure, as specifically seen in FIG. 14 , clinical information and blood may be collected, one or more blood biomarkers may be assessed, alone or in panel form, and a risk score and personalized medication options may be generated. In a variation, the risk score and/or personalized medication options may be presented in a report. As seen in FIG. 15 , another report, based on clinical and socio-demographic data, may provide, a CFI-S score, percentile, a risk rating, and treatment recommendations. In an example, the reports are electronic, and processed via a computer device, system or an app. In another example, the reports are printed on paper.

Additionally, in accordance with another aspect of the present disclosure, biomarkers useful for objectively predicting future hospitalization due to suicidality in subjects have been discovered. In one aspect, the present disclosure is directed to a universal method for future hospitalization due to suicidality in a subject; that is, a method for predicting future hospitalization due to suicidality across all psychiatric diagnoses and genders. The method includes obtaining a first expression level of a blood biomarker in an initial sample obtained from the subject; and determining a second expression level of the blood biomarker in a subsequent sample obtained from the subject, wherein an increase in the expression level of the blood biomarker in the subsequent sample obtained from the subject as compared to the expression level of the initial sample indicates a higher risk of future hospitalizations due to suicidality. In some embodiments, the methods further include obtaining clinical risk factor information and clinical scale data such as for anxiety, mood and/or psychosis from the subject in addition to obtaining a blood biomarker expression level in a sample obtained from the subject.

In another aspect, the present disclosure is directed to further mitigating suicidality in the subject(s) identified above. The method includes: obtaining an expression level of a blood biomarker in a sample obtained from the subject; obtaining a reference expression level of the blood biomarker; identifying a difference in the expression level of the blood biomarker in the sample as compared to the reference expression level of the blood biomarker; and, upon identifying a difference between the expression level of the blood biomarker in the sample obtained from the subject and the reference expression level of the blood biomarker, administering a treatment, wherein the treatment reduces the difference between the expression level of the blood biomarker in the sample as compared to the reference expression level of the blood biomarker to mitigate suicidality in the subject. As used herein, “mitigate”, “mitigating”, and the like refer to making a condition less severe and/or preventing a condition. More particularly, the phrase “mitigate suicidality” refers to reducing suicide ideation in a subject and/or preventing suicide completion.

Suitable treatments can be a lifestyle modification, administering a therapy, and combinations thereof.

Suitable therapy can be a nutritional, a drug and psychotherapy.

Particularly suitable nutritionals can be omega-3 fatty acids, including, by way of example, docosahexaenoic acid (DHA).

In some embodiments, the therapies can include drugs and natural compounds that have now been found to be effective in mitigating suicidality either universally or for a specific gender and/or psychiatric diagnosis. Exemplary repurposed drugs and natural compounds are found in Tables 6-18.

Various functions and advantages of these and other embodiments of the present disclosure will be more fully understood from the examples shown below. The examples are intended to illustrate the benefits of the present disclosure, but do not exemplify the full scope of the disclosure.

EXAMPLES

In this Example, blood biomarkers from three cohorts of subjects were analyzed.

Materials and Methods

Cohorts

Three independent cohorts were examined: discovery cohort (a live psychiatric participants cohort), validation cohort (a postmortem coroner's office cohort), and testing cohort (also referred to herein as “test cohort”) (an independent live psychiatric participants test cohort for predicting suicidal ideation, and for predicting future hospitalizations for suicidality) (FIG. 1A).

The live psychiatric participants are part of a larger longitudinal cohort of adults that are continuously being collected. Participants are recruited from the patient population at the Indianapolis VA Medical Center and Indiana University School of Medicine through referrals from care providers, the use of brochures left in plain sight in public places and mental health clinics, and through word of mouth. All participants understood and signed informed consent forms detailing the research goals, procedure, caveats and safeguards, per IRB approved protocol. Participants completed diagnostic assessments by an extensive structured clinical interview—Diagnostic Interview for Genetic Studies—at a baseline visit, followed by up to six testing visits, 3-6 months apart or whenever a new psychiatric hospitalization occurred. At each testing visit, they received a series of psychiatric rating scales, including the Hamilton Rating Scale for Depression-17, which includes a suicidal ideation (SI) rating item (FIG. 1B). Further, blood was drawn. Whole blood (10 ml) was collected in two RNA-stabilizing PAXgene tubes, labeled with an anonymized ID number, and stored at −80° C. in a locked freezer until the time of future processing. Whole-blood RNA was extracted for microarray gene expression studies from the PAXgene tubes, as detailed below.

The participant discovery cohort, from which the biomarker data were derived, consisted of 66 participants (49 males, 17 females) with psychiatric disorders and multiple testing visits, who each had at least one diametric change in SI scores from no SI to high SI from one testing visit to another. There were 2 participants with 6 visits each, 3 participants with 5 visits each, 5 participants with 4 visits each, 34 participants with 3 visits each, and 22 participants with 2 visits each resulting in a total of 193 blood samples for subsequent gene expression microarray studies (FIG. 1B and Table 2).

The postmortem validation cohort, in which the top biomarker findings were validated for behavior, consisted of 38 male and 7 female violent suicide completers obtained through the Marion County coroner's office (Table 2). A last observed alive postmortem interval of 24 h or less was required, and the cases selected had completed suicide by means other than overdose, which could affect gene expression. Thirty-one participants completed suicide by gunshot to head or chest, 12 by asphyxiation, 1 by slit wrist, and 1 by electrocution. Next of kin signed informed consent at the coroner's office for donation of blood for research.

The independent test cohort for predicting suicidal ideation (Table 2) consisted of 184 male and 42 female participants with psychiatric disorders, demographically matched with the discovery cohort, with one or multiple testing visits in the lab, with either no SI, intermediate SI, or high SI, resulting in a total of 226 blood samples in which whole-genome blood gene expression data were obtained (FIG. 1A and Table 2).

The test cohort for predicting future hospitalizations (FIG. 1A and Table 2) is a subset (170 males, 24 females) of the independent test cohort for which a longitudinal follow-up with electronic medical records was available. The participants' subsequent number of psychiatric hospitalizations, with or without suicidality (ideation or attempt), was tabulated from electronic medical records. Participants were evaluated for the presence of future hospitalizations for suicidality, and for the frequency of such hospitalizations. A hospitalization was deemed to be without suicidality if suicidality was not listed as a reason for admission, and no SI was described in the admission and discharge medical notes. Conversely, a hospitalization was deemed to be due to suicidality if suicidal acts or intent were listed as a reason for admission, and/or SI was described in the admission and discharge medical notes.

TABLE 2 Demographics Age Mean Universal Subjects Gender Diagnosis Ethnicity (SD) Discovery Discovery Cohort 66 Male = 49 BP = 25 EA = 51 47.94 (Longitudinal Within-Subject Female = 17 MDD = 17 AA = 14 (9.47) Changes in Suicidal Ideation) SZA = 9 Asian = 1 SZ = 4 PTSD = 8 MOOD = 2 PSYCH = 1 Validation Independent Validation Cohort 45 Male = 38 NP = 19 EA = 37 40.69 for Gene Expression Female = 7 MDD = 19 AA = 7 (16.93) (Suicide Completers) BP = 2 Hispanic = 1 SZ = 1 AX = 1 Alcoholism = 1 ADHD = 1 PTSD = 1 Testing All Independent Testing Cohort For 226 Male = 184 BP = 68 EA = 148 All Predicting State Female = 42 MDD = 32 AA = 73 50.26 (Suicidal Ideation at Time of SZA = 53 Asian = 1 (9.47) Assessment) SZ = 45 Hispanic = 3 No SI PTSD = 19 Mixed = 1 51.1 MOOD = 5 Intermediate PSYCH = 4 SI 49 High SI 44.3 Independent Testing Cohort 194 Male = 170 BP = 72 EA = 167 All = 50.04 For Predicting Trait Female = 24 MDD = 44 AA = 76 (9.11) (Hospitalizations for Suicidality SZA = 50 Hispanic = 3 No Hosp for in the Year Following SZ = 46 Mixed = 1 SI = 50.52 Assessment) PTSD = 24 Hosp for SI = 46.24 MOOD = 8 PSYCH = 3 Subtypes High Anxiety Subtype 46 Male = 40 BP = 13 EA = 27 All Female = 6 MDD = 10 AA = 19 50.96 SZA = 9 (7.63) SZ = 11 No SI PTSD = 2 52.1 (n = 44) MOOD = 1 Intermediate SI 52.5 (n = 4) High SI 39.4 (n = 5) Low Mood Subtype 76 Male = 57 BP = 21 EA = 53 All Female = 19 MDD = 17 AA = 20 51.53 SZA = 15 Hispanic = 2 (10.04) SZ = 15 Asian = 1 No SI PTSD = 6 51.44 (n = 58) MOOD = 1 Intermediate PSYCH = 1 SI 51.81 (n = 14) High SI 51.9 (n = 8) Combined Subtype 86 Male = 61 BP = 30 EA = 63 All Female = 25 MDD = 11 AA = 21 47.95 SZA = 21 Hispanic = 1 (9.36) SZ = 11 Mixed = 1 No SI PTSD = 11 50.79(n = 56) MOOD = 2 Intermediate SI 45.43 (n = 18) High SI 43.06 (n = 25) Non- 141 Male = 121 BP = 40 EA = 86 All Affective (Psychotic) Subtype Female = 20 MDD = 17 AA = 52 50.71 SZA = 35 Hispanic = 2 (9.49) SZ = 32 Mixed = 1 No SI PTSD = 10 50.89 (n = 132) MOOD = 4 Intermediate PSYCH = 3 SI 51.67 (n = 6) High SI 42.33 (n = 6) Age Mean Male Bipolar Subjects Gender Diagnosis Ethnicity (SD) Discovery Male Bipolar 20 Male = 20 BP = 20 EA = 20 48.12 Discovery Cohort (9.10) (Within-Subject Changes in Suicidal Ideation) Validation Male 38 Male = 38 NP = 18 EA = 31 40.82 Independent Validation Cohort MDD = 16 AA = 6 (17.31) for Gene Expression BP = 1 Hispanic = 1 (Suicide Completers) SZ = 1 AX = 1 Alcoholism = 1 Testing Male Bipolar 49 Male = 49 BP = 49 EA = 43 All Independent Testing Cohort For AA = 5 49.16 Predicting State Hispanic = 1 (10.01) (Suicidal Ideation at Time of No SI Assessment 50.19 Intermediate SI 48.73 High SI 40.42 Male Bipolar 44 Male = 44 BP = 44 EA = 39 All = 48.88 Independent Testing Cohort AA = 4 (10.23) For Predicting Trait Hispanic = 1 No Hosp for SI = 48.76 (Hospitalizations for Suicidality Hosp for SI = 52.25 in the Year Following Assessment)

Medications. The participants in the discovery cohort were all diagnosed with various psychiatric disorders (Table 2). Their psychiatric medications were listed in their electronic medical records, and documented at the time of each testing visit. The participants were on a variety of different psychiatric medications: mood stabilizers, antidepressants, antipsychotics, benzodiazepines and others (data not shown). Medications can have a strong influence on gene expression. However, the discovery of differentially expressed genes was based on within-participant analyses, which factor out not only genetic background effects but also minimizes medication effects, as the participants rarely had major medication changes between visits. Moreover, there was no consistent pattern in any particular type of medication, or between any change in medications and SI, in the rare instances where there were changes in medications between visits.

Blood Gene Expression Experiments

RNA extraction. Whole blood (2.5-5 ml) was collected into each PaxGene tube by routine venipuncture. PaxGene tubes contain proprietary reagents for the stabilization of RNA. RNA was extracted and processed as described in Le-Niculescu et al., Mol Psychiatry 2013; 18(12): 1249-1264.

Microarrays. Microarray work was carried out using methodology described in Niculescu et al., Mol Psychiatry 2015; 20(11): 1266-1285.

Biomarkers

Discovery Cohort

The participant's suicidality score from the item in the Hamilton Rating Scale for Depression (HAMD SI) assessed at the time of blood collection (FIG. 1G) was used. The gene expression differences were analyzed between the no SI (a score of 0) and the high SI (a score of 2 and above) visits, using a powerful within-participant design, then an across-participants summation (FIG. 1F).

The data was analyzed in two ways: an Absent-Present (AP) approach, and a differential expression (DE) approach. The AP approach may capture turning on and off of genes, and the DE approach may capture gradual changes in expression.

For the AP approach, Affymetrix Microarray Suite Version 5.0 (MASS) was used to generate Absent (A), Marginal (M), or Present (P) calls for each probeset on the chip (Affymetrix U133 Plus 2.0 GeneChips) for all participants in the discovery cohort (Affymetrix Inc., Santa Clara, Calif.). For the DE approach, all Affymetrix microarray data was imported as .cel files into Partek Genomic Suites 6.6 software package (Partek Incorporated, St Louis, Mich., USA). Using only the perfect match values, a robust multi-array analysis (RMA) was conducted, background corrected with quantile normalization and a median polish probeset summarization, to obtain the normalized expression levels of all probesets for each chip. RMA was performed independently for each gender and diagnosis subgroup used in the Example, to avoid potential artefacts due to different ranges of gene expression in different gender and diagnoses. Then the participants' normalized data was extracted from these gender and diagnosis RMAs and assembled for the different cohorts used in the Example.

A/P analysis. For the longitudinal within-participant AP analysis, comparisons were made within-participant between sequential visits to identify changes in gene expression from Absent to Present that track changes in phene expression (suicidal ideation) from No SI to High SI, as described in Niculescu et al., Mol Psychiatry 2015; 20(11): 1266-1285 and Levey et al., Mol Psychiatry 2016; 21(6): 768-785. For a comparison between two sequential visits, if there was a change from A to P tracking a change from No SI to High SI, or a change from P to A tracking a change from High SI to No SI, that was given a score of +1 (increased biomarker in High SI). If the change was in opposite direction in the gene versus the phene (which is SI), that was given a score of −1 (decreased biomarker in High SI). If there was no change in gene expression between visits despite a change of phene expression (SI levels), or a change in gene expression between visits despite no change in phene expression (SI levels), that was given a score of 0 (not tracking as a biomarker). If there was no change in gene expression and no change in suicidal ideation between visits, that was given a score of +1 if there was concordance (P-P with High SI-High SI, or A-A with No SI-No SI), or a score of −1 if there was the opposite (A-A with High SI-High SI, or P-P with No SI-No SI). If the changes were to M (moderate) instead of P, the values used were 0.5 or −0.5. These values were then summed up across the comparisons in each participant, resulting in an overall score for each gene/probeset in each participant. A perfection bonus was also used. If the gene expression perfectly tracked the suicidal ideation in a participant that had at least two comparisons (3 visits), that probeset was rewarded by a doubling of its overall score. Additionally, a non-tracking correction was used. If there was no change in gene expression in any of the comparisons for a particular participant, that overall score for that probeset in that participant was zero. An R script was developed to conduct the calculations, and the analysis was double-checked manually using formulas/macros in Excel.

DE analysis. For the longitudinal within-participant DE analysis, fold changes (FC) in gene expression were calculated between sequential visits within each participant, as described in Niculescu et al., Mol Psychiatry 2015; 20(11): 1266-1285 and Levey et al., Mol Psychiatry 2016; 21(6): 768-785. Scoring methodology was similar to that used above for AP. Probesets that had a FC≥1.2 were scored +1 (increased in High SI) or −1 (decreased in High SI). FC≥1.1 were scored +0.5 or −0.5. FC lower than 1.1 were considered no change. The only difference between the DE and the AP analyses was when scoring comparisons where there was no phene expression (SI) change between visits and no change in gene expression between visits (FC lower than 1.1). In that case, the comparison received the same score as the nearest preceding comparison where there was a change in SI from visit to visit. If no preceding comparison with a change in SI was available, then it was given the same score as the nearest subsequent comparison where there was a change in SI. A perfection bonus and a non-tracking correction were also used for the DE analysis. If the gene expression perfectly tracked the suicidal ideation in a participant that had at least two comparisons (3 visits), that probeset was rewarded by a doubling of its score. If there was no change in gene expression in any of the comparisons for a particular participant, that overall score for that probeset in that participant was zero. An R script was developed to conduct the calculations, and the analysis was double-checked manually using formulas/macros in Excel.

Internal score. Once scores within each participant were calculated, an algebraic sum across all participants was obtained, for each probeset. Probesets were then given internal points based upon these algebraic sum scores. Probesets with scores above the 33.3% of the maximum score (for increased probesets, respectively for decreased probesets) received 1 point, those above 50% received 2 points, and those above 80% received 4 points. For AP analyses, 35 probesets received 4 points, 754 probesets received 2 points, and 2197 probesets received 1 point, for a total of 2986 probesets. For DE analyses, 35 probesets received 4 points, 1477 probesets received 2 points, and 6450 probesets received 1 point, for a total of 9829 probesets. The overlap between the two discovery methods for probesets with an internal score of 1 is shown in FIG. 1D. Different probesets may be found by the two methods due to differences in scope (DE is also capturing genes that are present in both visits of a comparison, i.e. PP, but are changed in expression), thresholds (what makes the 33.3% change cutoff across participants varies between methods), and technical detection levels (what is considered in the noise range varies between the methods).

Gene Symbol for the probesets were identified using NetAffyx (Affymetrix) for Affymetrix HG-U133 Plus 2.0 GeneChips, followed by GeneCards to confirm the primary gene symbol. In addition, for those probesets that were not assigned a gene symbol by NetAffyx, GeneAnnot (https://genecards.weizmann.ac.il/geneannot/index.shtml) was used to obtain a gene symbol for these uncharacterized probesets, followed by GeneCard. Genes were then scored using manually curated CFG databases as described below (FIG. 1E).

Prioritization Using Convergent Functional Genomics (CFG)

Databases. Manually curated databases were established of the human gene expression/protein expression studies (postmortem brain, peripheral tissue/fluids: CSF, blood and cell cultures), human genetic studies (association, copy number variations and linkage), and animal model gene expression and genetic studies, published to date on psychiatric disorders. Only the findings deemed significant in the primary publication, using the particular experimental design and thresholds, are included in the databases. The databases include only primary literature data and do not include review papers or other secondary data integration analyses to avoid redundancy and circularity. These large and constantly updated databases have been used in the CFG cross validation and prioritization platform (FIG. 1E). For this Example, data from 454 papers on suicide were present in the databases at the time of the CFG analyses (genetic studies-170, brain studies-197, peripheral fluids-87).

Human postmortem brain gene expression/protein expression evidence. Converging evidence was scored for a gene if there were published reports of human postmortem data showing changes in expression of that gene or changes in protein levels in brains from participants who died from suicide.

Human blood, CSF, and other peripheral tissue gene expression/protein expression evidence. Converging evidence was scored for a gene if there were published reports of human blood, lymphoblastoid cell lines, cerebrospinal fluid, or other peripheral tissue data showing changes in expression of that gene or changes in protein levels in participants who had a history of suicidality or who died from suicide.

Human genetic evidence (association, linkage). To designate convergence for a particular gene, the gene had to have independent published evidence of association or linkage for suicide. For linkage, the physical positions (bp) of each gene were obtained through GeneCards (http://www.genecards.org), and the sex averaged cM location of the start of the gene was then obtained through http://compgen.rutgers.edu/map_interpolator.shtml. For linkage convergence, the start of the gene had to map within 5 cM of the location of a marker linked to the disorder.

CFG scoring. For CFG analysis (FIG. 1E), the external cross-validating lines of evidence were weighted such that findings in human postmortem brain tissue, the target organ, were prioritized over peripheral tissue/fluid findings and genetic findings, by giving them twice as many points. Human brain expression evidence was given 4 points, whereas human peripheral evidence was given 2 points, and human genetic evidence was given a maximum of 2 points for association, and 1 point for linkage. Each line of evidence was capped in such a way that any positive findings within that line of evidence resulted in maximum points, regardless of how many different studies support that single line of evidence, to avoid potential popularity biases. In addition to the external CFG score, genes were prioritized based upon the initial gene expression analyses used to identify them, giving them an internal score. Probesets identified by gene expression analyses could receive a maximum of 4 points. Thus, the maximum possible total CFG score for each gene was 12 points (4 points for the internal score and 8 points for the external CFG score) (Tables 3A-3F). The scoring system was decided upon before the analyses were carried out. Twice as much weight was given to the external score as compared to the internal score in order to increase generalizability and avoid fit to cohort of the prioritized genes. This scoring system provides a good separation of genes based on gene expression evidence and on independent cross-validating evidence in the field (FIG. 1E). In the future, with multiple large datasets, machine learning approaches could be used and validated to assign weights to CFG.

TABLE 3 Direction of Affymetrix Probe Gene Change in Top Dozen Bio- Set ID Symbol Suicidality Analysis marker from: Top Predictor Biomarker for: A: Universal Biomarkers for Suicidality - Top Dozen and Top Predictor Biomarkers. D—Decreased, I—Increased. AP—Absent/Present, DE—Differential Expression 224240_s_at CCL28 D AP Discovery 213541_s_at ERG D DE Discovery 242572_at GAB1 I AP Discovery 214540_at HIST1H2BO I DE Discovery 210354_at IFNG D AP Prioritization 225686_at SKA2 D DE Prioritization 210739_x_at SLC4A4 I AP Prioritization Suicidal ideation state - cross-sectional 218832_x_at ARRB1 D AP Validation 57082_at LDLRAP1 D DE Validation 212226_s_at PPAP2B I AP Validation 2215078_at SOD2 I Future hospitalizations for suicidality - all future years - cross-sectional 203680_at PRKAR2B D Future hospitalizations for suicidality - all future years - longitudinal 209534_x_at AKAP13 I Future hospitalizations for suicidality - all future years - longitudinal 237180_at PSME4 I DE Validation Future hospitalizations for suicidality - in the first year - cross-sectional 209000_s_at SEPT8 I Future hospitalizations for suicidality - in the first year - longitudinal 218062_x_at CDC42EP4 D Future hospitalizations for suicidality - in the first year - cross-sectional 214252_s_at CLN5 D Suicidal ideation state - cross-sectional Future hospitalizations for suicidality - all future years - cross-sectional 232526_at ITPKB I Suicidal ideation state - longitudinal 209677_at PRKCI D Suicidal ideation state - longitudinal 244130_at HTR2A I DE Prioritization Suicidal ideation state - longitudinal B. Biomarkers for Suicidality in Males - Top Dozen and Top Predictor Biomarkers. D—Decreased, I—Increased. AP—Absent/Present, DE—Differential Expression 227351_at C16orf52 D AP Discovery 203032_s_at FH D DE Discovery 214540_at HIST1H2BO I DE Discovery 242538_at TFDP1 I AP Discovery 225686_at SKA2 D AP, DE Prioritization 210739_x_at SLC4A4 I AP Prioritization 241811_x_at SLC6A4 I DE Prioritization 57082_at LDLRAP1 D DE Validation 210592_s_at SAT1 I DE Validation 209386_at TM4SF1 I AP Validation 239991_at ZMYND8 D AP Validation 218174_s_at TMEM254 D Suicidal ideation state - longitudinal 200009_at GDI2 D Suicidal ideation state - cross-sectional 207194_s_at ICAM4 D Future hospitalizations for suicidality - in first year - cross-sectional 203336_s_at ITGB1BP1 D Future hospitalizations for suicidality - all future years - longitudinal 201460_at MAPKAPK2 I Suicidal ideation state - cross-sectional Future hospitalizations for suicidality - all future years - cross-sectional 237180_at PSME4 I Future hospitalizations for suicidality - in first year - cross-sectional 224758_at C7orf73 D Future hospitalizations for suicidality - in first year - longitudinal 214252_s_at CLN5 D Future hospitalizations for suicidality - all future years - cross-sectional 244677_at PER1 I Future hospitalizations for suicidality - all future years - longitudinal C. Biomarkers for Suicidality in Females - Top Dozen and Top Predictor Biomarkers. D—Decreased, I—Increased. AP—Absent/Present, DE—Differential Expression 1566183_at Hs.637764 I AP Discovery Suicidal ideation state 243713_at Hs.661328 I DE Discovery 217369_at IGHG1 D AP Discovery 1556842_at LOC286087 D DE Discovery 244019_at T89845 I AP Discovery 219025_at CD248 I AP Prioritization 236804_at COMT I AP Prioritization 244130_at HTR2A I DE Prioritization 210354_at IFNG D AP Prioritization 210354_at IFNG D DE Prioritization 240226_at AA828246 I DE Validation 1568903_at Hs.736359 D AP Validation 201185_at HTRA1 I AP Validation 220005_at P2RY13 D DE Validation 210486_at ANKMY1 D Suicidal ideation state - cross-sectional 1569022_a_at PIK3C2A I Future hospitalizations for suicidality - in first year - longitudinal Future hospitalizations for suicidality - all future years - longitudinal 215078_at SOD2 I Future hospitalizations for suicidality - all future years - longitudinal Direction of Affymetrix Change in Top Dozen Probe Set ID Gene Symbol Suicidality Analysis Biomarker from: Top Bonferroni Predictor Biomarker for: D. Biomarkers for Suicidality in Bipolar Disorder - Top Dozen and Top Bonferroni Predictor Biomarkers. D—Decreased, I—Increased. A—Absent/Present, DE—Differential Expression 236879_at BF114768 I DE Discovery 1562416_at FLNB I AP Discovery 231262_at Hs.147375 D DE Discovery 1557984_s_at RPAP3 D AP Discovery 239683_at CLYBL D AP Prioritization 244130_at HTR2A I DE Prioritization 207519_at SLC6A4 D DE Prioritization 1563357_at TNF I AP Prioritization 218081_at C20orf27 D DE Validation 203394_s_at HES1 I AP Validation 214144_at POLR2D D AP Validation 213988_s_at SAT1 I DE Validation 232526_at ITPKB I Suicidal ideation state - cross-sectional 224758_at C7orf73 D Suicidal ideation state - cross-sectional 208889_s_at NCOR2 D Suicidal ideation state - longitudinal 214433_s_at SELENBP1 D Future hospitalizations for suicidality - in first year - cross-sectional Future hospitalizations for suicidality - all future years - cross-sectional 219862_s_at NARF I Future hospitalizations for suicidality - in first year - cross-sectional 201466_s_at JUN I Future hospitalizations for suicidality - in first year - longitudinal 237180_at PSME4 I Future hospitalizations for suicidality - all future years - cross-sectional Direction of Affymetrix Gene Change in Top Dozen Probe Set ID Symbol Suicidality Analysis Biomarker from: Top Predictor Biomarker for: E. Biomarkers for Suicidality in Depression - Top Dozen and Top Predictor Biomarkers. D—Decreased, I—Increased. AP—Absent/Present, DE—Differential Expression 35201_at HNRNPL D DE Discovery 1556828_at MNATI I DE Discovery 218509_at PLPPR2 I AP, DE Discovery 222351_at PPP2R1B D AP Discovery 219243_at GIMAP4 D DE Discovery and Validation 1554808_at ACP1 D AP Prioritization 239367_at BDNF I DE Prioritization 209560_s_at DLK1 I AP, DE Prioritization 206462_s_at NTRK3 I AP, DE Prioritization 225686_at SKA2 D DE Prioritization 236527_at ATP6V0E1 D AP Validation 1554264_at CKAP2 I AP Validation Future hospitalizations for suicidality 201465_s_at JUN I DE Validation 241453_at PTK2 I Suicidal ideation state - cross-sectional Future hospitalizations for suicidality - in first year - cross-sectional 214085_x_at GLIPR1 D Suicidal ideation state - cross-sectional 232633_at XRCC5 D Suicidal ideation state - longitudinal 1554610_at ANKMY1 D Future hospitalizations for suicidality - in first year - cross-sectional Future hospitalizations for suicidality - all future years - cross-sectional 204850_s_at DCX D Future hospitalizations for suicidality - in first year - longitudinal F. Biomarkers for Suicidality in Males with Bipolar Disorder - Top Dozen and Top Predictor Biomarkers. D—Decreased, I—Increased. AP—Absent/Present, DE—Differential Expression 239711_at ADAL D AP Discovery Future hospitalizations for suicidality 237259_at BE674182 I DE Discovery 208299_at CACNA1I I AP Discovery 207194_s_at ICAM4 D DE Discovery 239683_at CLYBL D AP Prioritization 214619_at CRHR1 D DE Prioritization 244130_at HTR2A I DE Prioritization 213769_at KSR1 I AP Prioritization 218081_at C20orf27 D DE Validation 214144_at POLR2D D AP Validation 213988_s_at SAT1 I DE Validation 215918_s_at SPTBN1 I AP Validation Suicidal ideation state - cross-sectional 224758_at C7orf73 D Suicidal ideation state - cross-sectional 234332_at NUB1 I Suicidal ideation state - longitudinal 205481_at ADORA1 D Suicidal ideation state - longitudinal 222176_at PTEN I Future hospitalizations for suicidality - in first year - cross-sectional 214433_s_at SELENBP1 D Future hospitalizations for suicidality - in first year - cross-sectional 237180_at PSME4 I Future hospitalizations for suicidality - all future years - cross-sectional 210377_at ACSM3 D Future hospitalizations for suicidality - all future years - cross-sectional Direction of Top Dozen Affymetrix Probe Change in Biomarker Set ID Gene Symbol Suicidality Analysis from: Top Bonferroni Predictor Biomarker for: G. Biomarkers for Suicidality in Males with Depression - Top Dozen and Top Predictor Biomarkers. D—Decreased, I—Increased. AP—Absent/Present, DE—Differential Expression 234681_s_at CHD6 I AP Discovery 223974_at DLGAP1- I DE Discovery AS2 35201_at HNRNPL D DE Discovery 237951_at R02328 I DE Discovery 215263_at ZXDA D AP Discovery 209560_s_at DLK1 I AP Prioritization 214170_x_at FH D DE Prioritization 236587_at LRRC6 I DE Prioritization 217033_x_at NTRK3 D AP Prioritization 236527_at ATP6V0E1 D AP Validation 213524_s_at G0S2 I DE Validation 226687_at PRPF40A D DE Validation 209841_s_at LRRN3 D Suicidal ideation state - cross-sectional 241453_at PTK2 I Suicidal ideation state - cross-sectional 210192_at ATP8A1 I Suicidal ideation state - longitudinal Future hospitalizations for suicidality - all future years - longitudinal 228305_at ZNF565 D Suicidal ideation state - longitudinal 1554610_at ANKMY1 D Future hospitalizations for suicidality - in first year - cross-sectional Future hospitalizations for suicidality - all future years - cross-sectional 205898_at CX3CR1 D Future hospitalizations for suicidality - in first year - longitudinal 213524_s_at G0S2 I Future hospitalizations for suicidality - all future years - cross-sectional Direction of Affymetrix Probe Set Change in ID Gene Symbol Suicidality Top Predictor Biomarker for: H. Biomarkers for Suicidality in Males with Post-Traumatic Stress Disorder (PTSD) - Top Predictor Biomarkers. D—Decreased, I—Increased. 237180_at PSME4 I Suicidal ideation state - cross-sectional Future hospitalizations for suicidality - all future years - cross-sectional 209841_s_at LRRN3 D Suicidal ideation state - cross-sectional 209677_at PRKCI D Suicidal ideation state - longitudinal 229331_at SPATA18 I Suicidal ideation state - longitudinal Future hospitalizations for suicidality - in first year - longitudinal Future hospitalizations for suicidality - all future years - longitudinal 214252_s_at CLN5 D Future hospitalizations for suicidality - in first year - cross-sectional 212226_s_at PPAP2B I Future hospitalizations for suicidality - in first year - cross-sectional 202259_s_at N4BP2L2 D Future hospitalizations for suicidality - all future years - cross-sectional 238919_at PCDH9 D Future hospitalizations for suicidality - all future years - longitudinal I. Biomarkers for Suicidality in Males with Schizophrenia/Schizoaffective Disorder - Top Predictor Biomarkers. D—Decreased, I—Increased. 205996_s_at AK2 D Suicidal ideation state - cross-sectional 205858_at NGFR I Suicidal ideation state - cross-sectional Suicidal ideation state - longitudinal 236527_at ATP6V0E1 D Suicidal ideation state - longitudinal Future hospitalizations for suicidality - in first year - cross-sectional 218062_x_at CDC42EP4 D Future hospitalizations for suicidality - in first year - longitudinal 229331_at SPATA18 I Future hospitalizations for suicidality - in first year - longitudinal 1557966_x_at MTERF4 D Future hospitalizations for suicidality - all future years - cross-sectional 212226_s_at PPAP2B I Future hospitalizations for suicidality - all future years - cross-sectional 213321_at BCKDHB D Future hospitalizations for suicidality - all future years - longitudinal J. Biomarkers for Suicidality in High Anxiety Subtype - Top Predictor Biomarkers. D—Decreased, I—Increased. 209677_at PRKCI D Suicidal ideation state - cross-sectional Future hospitalizations for suicidality - all future years - longitudinal 218656_s_at LHFP I Suicidal ideation state - cross-sectional 204036_at LPAR1 D Future hospitalizations for suicidality - in first year - cross-sectional 214540_at HIST1H2BO I Future hospitalizations for suicidality - in first year - cross-sectional Future hospitalizations for suicidality - all future years - cross-sectional 236879_at BF114768 I Future hospitalizations for suicidality - all future years - longitudinal 216765_at MAP2K5 D Future hospitalizations for suicidality - all future years - cross-sectional K. Biomarkers for Suicidality in Low Mood Subtype - Top Predictor Biomarkers. D—Decreased, I—Increased. 209534_x_at AKAP13 I Suicidal ideation state - longitudinal 231772_x_at CENPH D Suicidal ideation state - longitudinal 207844_at IL13 I Suicidal ideation state - cross-sectional 214252_s_at CLN5 D Suicidal ideation state - cross-sectional 230191_at TTBK1 D Future hospitalizations for suicidality - in first year - longitudinal 237180_at PSME4 I Future hospitalizations for suicidality - in first year - longitudinal Future hospitalizations for suicidality - all future years - cross-sectional 231854_at PIK3CA D Future hospitalizations for suicidality - in first year - cross-sectional 214782_at CTTN I Future hospitalizations for suicidality - in first year - cross-sectional 211633_x_at IGHG1 D Future hospitalizations for suicidality - all future years - longitudinal L. Biomarkers for Suicidality in the High Psychosis (Non-Affective) Subtype - Top Predictor Biomarkers. D—Decreased, I—Increased. 231854_at PIK3CA D Suicidal ideation state - cross-sectional 204730_at RIMS3 D Future hospitalizations for suicidality - in first year - cross-sectional 215078_at SOD2 I Future hospitalizations for suicidality - in first year - cross-sectional 229856_s_at PITHD1 D Future hospitalizations for suicidality - all future years - longitudinal 215078_at SOD2 I Future hospitalizations for suicidality - all future years - longitudinal Future hospitalizations for suicidality - all future years - cross-sectional 203336_s_at ITGB1BP1 D Future hospitalizations for suicidality - all future years - cross-sectional M. Biomarkers for Suicidality in the Combined (Affective) Subtype - Top Predictor Biomarkers. D—Decreased, I—Increased. 209677_at PRKCI D Suicidal ideation state - longitudinal Future hospitalizations for suicidality - all future years - longitudinal 566861_at GATM1 I Suicidal ideation state - longitudinal 214782_at CTTN I Future hospitalizations for suicidality - in first year - longitudinal 228305_at ZNF565 D Future hospitalizations for suicidality - in first year - longitudinal 201929_s_at PKP4 D Future hospitalizations for suicidality - in first year - cross-sectional 236879_at BF114768 I Future hospitalizations for suicidality - all future years - longitudinal 1557966_x_at MTERF4 D Future hospitalizations for suicidality - all future years - cross-sectional 232526_at ITPKB I Future hospitalizations for suicidality - all future years - cross-sectional Validation Analyses

For the AP analyses, the Affymetrix microarray .chp data files from the participants in the coroner validation cohort of suicide completers were imported into the MASS Affymetrix Expression Console, alongside the data files from the No SI and High SI groups in the live discovery cohort. The AP data was transferred to an Excel sheet and transformed: A into 0, M into 0.5, and P into 1. All data was then Z-scored together by gender. If a probeset would have showed no variance and thus gave a non-determined (0/0) value in Z-scoring in a gender, the values were excluded from that probeset for that gender from the analysis. All probesets, however, did show variance in this Example.

For the DE analyses, Affymetrix microarray .cel files were imported from the participants in the validation cohort of suicide completers into Partek Genomic Suites. An RMA was run by gender, background corrected with quantile normalization, and a median polish probeset summarization of the chips from the validation cohort was conducted to obtain the normalized expression levels of all probesets for each chip. The No SI and High SI groups from the discovery cohort were RMA by gender and diagnosis, as described above for Discovery. Partek normalizes expression data into a log base of 2 for visualization purposes. Expression data was non-log transformed by taking 2 to the power of the transformed expression value, and the non-log transformed coroner validation cohort expression data was transferred to an Excel sheet, alongside data from the No SI and High SI groups from the discovery cohort. All data was then Z-scored together by gender.

Validation analyses of the candidate biomarker genes were conducted separately for AP and for DE. The top candidate genes (total CFG score of 4 or above), were stepwise changed in expression from the No SI group to the High SI group to the suicide completers group. A CFG score of 4 or above reflects an empirical cutoff of 33.3% of the maximum possible CFG score of 12, which permits the inclusion of potentially novel genes with maximal internal score of 4, but no external evidence score. The Excel sheets with the Z-scored by gender expression data from AP were imported, respectively from DE, into Partek, and statistical analyses were performed using a one-way ANOVA for the stepwise changed probesets, and stringent Bonferroni corrections for all the probesets tested in AP and DE (stepwise and non-stepwise) (FIG. 1F).

Discovery and Validation in Male Bipolars

For male bipolar disorders, the discovery and validation were conducted as described above except that only male bipolar subjects from the discovery cohort (n=20 subjects, 65 visits) were used for discovery, and male suicide completers (n=38) were used for validation.

Phenotypic Measures

SASS. The Simplified Affective State Scale (SASS) is an 11-item scale for measuring mood state (SMS) and anxiety state (SAS), previously developed and described in Niculescu et al., Mol Psychiatry 2015; 20(11): 1266-1285 and Niculescu et al., American journal of medical genetics Part B, Neuropsychiatric genetics: the official publication of the International Society of Psychiatric Genetics 2006; 141B(6): 653-662. The SASS has a set of 11 visual analog scales (7 for mood, 4 for anxiety) each item ranging from 0 to 100 for mood state, and the same for anxiety state. The averaged 7 items for mood give the Mood score, and the averaged 4 items for anxiety give the Anxiety score.

CFI-S. Convergent Functional Information for Suicidality (CFI-S) (FIG. 4A) is a 22-item scale and Android app for suicide risk, which integrates, in a simple binary fashion (Yes-1, No-0), similar to a polygenic risk score, information about known life events, mental health, physical health, stress, addictions, and cultural factors that can influence suicide risk. The scale was administered at participant testing visits (263), or scored based on retrospective electronic medical record information and Diagnostic Interview for Genetic Testing (DIGS) information (457). When information was not available for an item, it was not scored (NA). The average of the score of the items for which there was information gives us the CFI-S score.

Subtypes

In order to identify possible subtypes of suicidality, a two-way unsupervised hierarchical clustering of the high SI visits in the discovery cohort, based on measures of anxiety and mood (from the SASS), as well as psychosis (PANS S Positive) was used. The mood item was inverted for the purposes of this analysis so that higher values indicate low mood. This clustering was used to identify four distinct subtypes of suicidality/high suicidal ideation: a high anxiety subtype, a low mood subtype, a combined affective subtype, and a non-affective (psychotic) subtype (FIG. 1C).

The insight from the discovery cohort was used to divide the independent test cohort into the four subtypes, using anxiety and mood measures from SASS, which are on a scale of 0 to 100. The high anxiety subtype participant visits had anxiety above 50 and low mood below 50, the low mood subtype had low mood below 50 and anxiety below 50, the combined affective subtype had low mood above 50 and anxiety above 50, and the non-affective subtype had low mood below 50 and anxiety below 50.

Combining Biomarkers and Phenotypic Measures

The Universal Predictor for Suicidality (UP-Suicide) construct, the primary endpoint, was decided upon as part of the apriori study design. It combines the top biomarkers with the phenomic (clinical) measures (CFI-S score, Mood and Anxiety scores from SASS). It is calculated as the simple algebraic summation of the components included (averaged panel of biomarkers (BioM), CFI-S, Mood, Anxiety). All individual biomarkers and clinical measure scores are Z-scored by gender and diagnosis, to normalize for different ranges of values and be able to combine them into a composite predictor (UP-Suicide). Decreased biomarkers, and Mood, have a minus sign in front of them.

Diagnostics

The test cohort for predicting suicidal ideation (state), and the subset of it that is a test cohort for predicting future hospitalizations for suicidality (trait), were assembled out of data that was RMA normalized by gender and diagnosis. The cohort was completely independent, there was no subject overlap with the discovery cohort. Phenomic (clinical) and gene expression markers used for predictions were Z-scored by gender and diagnosis, to be able to combine different markers into panels and to avoid potential artefacts due to different ranges of expression in different gender and diagnoses. Markers were combined by simple summation of the increased risk markers minus the decreased risk markers. Predictions were performed using R-studio. For cross-sectional analyses, marker expression levels were used, z-scored by gender and diagnosis. For longitudinal analyses, four measures were combined: marker expression levels, slope (defined as ratio of levels at current testing visit vs. previous visit, divided by time between visits), maximum levels (at any of the current or past visits), and maximum slope (between any adjacent current or past visits). For decreased markers, the minimum, rather than the maximum, was used for level calculations. All four measures were Z-scored then combined in an additive fashion into a single measure. The longitudinal analysis was carried out in a sub-cohort of the testing cohort consisting of participants that had at least two test visits.

Predicting High Suicidal Ideation State. Receiver-operating characteristic (ROC) analyses between genomic and phenomic marker levels and suicidal ideation (SI) were performed by assigning participants with a HAMD-SI score of 2 and greater into the high SI category. The pROC function of the R studio was used. The Z-scored biomarker and phene scores were used, running them in this ROC generating program against the “diagnostic” groups in the independent test cohort (high SI vs. the rest of subjects). Additionally, ANOVA was performed between no SI (HAMD-SI 0), intermediate (HAMD-SI 1), and high SI participants (HAMD-SI 2 and above) and Pearson R (one-tail) was calculated between HAMD-SI scores and marker levels (Tables 4A & 4B, FIGS. 5A-5C & FIG. 6 ).

TABLE 4 Diagnostics. Biomarkers, Phenes, and Combined Predictions. Red - top increased biomarker predictor; Blue - top decreased biomarker predictor. Underlined are individual biomarkers from the Top Dozen list, the others are from the Bonferroni list. For Universal, the panel of Top Dozen biomarkers is called BioM 12, and the panel of Bonferroni biomarkers is called BioM148, reflecting the number of markers in the panel. For Male Bipolar, the panel of Top Dozen biomarkers is called BioM 12, and the panel of Bonferroni biomarkers is called BioM54, reflecting the number of markers in the panel. Italic - a priori primary endpoint (UP-Suicide). A. Suicidal Ideation State Bold - p-value of AUC survives correction for multiple testing for predictions. ROC AUC is apriori primary predictive tool. Suicidality Participants Severity with high (HAMD SI Score) SI/Participants ROC AUC/ Correlation R/ T-test Predictors Cohort total p-value p-value p-value Universal Best Biomarkers SLC4A4 All 52/544 0.64/3.83E−04 0.13/1.54E−03 1.50E−03 CLN5 All 52/544 0.65/1.86E−04 −0.11/6.13E−03  3.90E−04 BioM 148 Panel All 52/544 0.61/6.18E−03 0.069/5.33E−02  1.77E−02 (Bonferroni List) BioM 12 Panel All 52/544 0.61/3.66E−03 0.12/3.02E−03 3.08E−03 (Top Dozen List) BioM 2 Panel All 52/544 0.66/4.92E−05 0.14/7.82E−04 1.90E−04 (SLC4A4 and CLN5) Phenes Mood All 52/544 0.77/5.93E−11 −0.38/3.17E−20  1.95E−10 Anxiety All 52/544 0.77/3.43E−11 0.31/8.60E−14 2.03E−12 Mood and Anxiety (SASS) All 52/544 0.81/5.55E−14 0.40/3.66E−22 3.57E−14 CFI-S All 52/523 0.86/9.98E−18 0.43/1.03E−24 5.46E−16 Mood and Anxiety and CFI-S All 52/523 0.89/2.59E−20 0.49/1.60E−33 1.08E−18 Phenes and Biomarkers Mood and Anxiety and CFI-S All 52/523 0.89/1.36E−20 0.49/2.84E−33 2.88E−18 and BioM 148 Mood and Anxiety and CFI-S All 52/523 0.90/3.87E−21 0.50/5.91−35 3.42E−19 and BioM 12(UP-Suicide) Mood and Anxiety and CFI-S All 52/523 0.89/4.56E−21 0.50/4.07E−34 2.83E−18 and BioM2 Male Bipolar Best Biomarkers SPTBN1 M-BP 12/130 0.72/6.62E−03 0.21/8.54E−03 9.05E−03 C7orf73 M-BP 12/130 0.75/2.38E−03 −0.17/2.76E−02  1.08E−04 BioM 54 Panel M-BP 12/130 0.49/5.29E−01   0/4.90E−01 7.12E−01 (Bonferroni List) BioM 12 Panel M-BP 12/130 0.57/2.08E−01 0.08/1.78E−01 8.79E−02 (Top Dozen List) BioM 2 M-BP 12/130 0.80/3.54E−04 0.23/4.77E−03 6.62E−05 (SPTBN1 and C7orf73) Phenes Mood M-BP 12/130  0.8/3.65E−04 −0.47/6.83E−09  1.65E−03 Anxiety M-BP 12/130 0.86/2.19E−05 0.41/7.09E−07 1.91E−05 Mood and Anxiety (SASS) M-BP 12/130 0.86/1.66E−05  0.5/7.15E−10 5.66E−05 CFI-S M-BP 12/128 0.92/1.10E−06  0.5/6.11E−10 1.31E−06 Mood and Anxiety and CFI-S M-BP 12/128 0.94/2.82E−07 0.61/1.24E−14 3.01E−06 Phenes and Biomarkers Mood and Anxiety and CFI-S M-BP 12/128 0.93/5.30E−07 0.61/1.78E−14 5.54E−06 and BioM 54 Mood and Anxiety and CFI-S M-BP 12/128 0.95/1.62E−07 0.62/1.92E−15 8.31E−07 and BioM 12 Mood and Anxiety and CFI-S M-BP 12/128 0.97/5.14E−08 0.64/2.29E−16 2.59E−07 and BioM 2 B. Future Hospitalizations for Suicidality in the First Year Following Assessment in the Independent Test Cohort Bold - p-value of AUC survives correction for multiple testing for predictions. ROC AUC is our apriori primary predictive tool. HAMD SI is the suicide rating question from the Hamilton Rating Scale for Depression. *Smaller cohort, as not everybody had HAMD SI information. Participants with Frequency of future future hospitalizations hospitalizations for for suicidality suicidality within within the first the first year Cox Regression year/Particpants ROC AUC/ Correlation T-test Hazard Ratio/ Predictors Cohort total p-value R/p-value p-value P-value Universal Best Biomarkers PSME4 All 38/471  0.59/2.62E−02 0.08/4.12E−02 6.20E−02 1.23/1.56E−01 AK2 All 38/471  0.60/2.31E−02 −0.06/9.70E−02  9.39E−03 1.35/7.22E−02 BioM 148 Panel All 38/471  0.52/3.37E−01 −0.02/6.67E−01  4.18E−01 1.09/8.27E−01 (Bonferroni List) BioM 12 Panel All 38/471  0.58/4.20E−02 0.05/1.47E−01 5.02E−02 1.88/1.41E−01 (Top Dozen List) BioM 2 Panel All 38/471  0.65/1.10E−03 0.10/1.29E−02 1.35E−03 1.68/0.018 (PSME4 and AK2) Phenes Mood All 38/471  0.65/1.00E−03 −0.16/3.63E−04  1.03E−03 1.69/1.47E−03 Anxiety All 38/471  0.69/3.70E−05 0.16/3.43E−04 2.30E−04 1.82/2.62E−04 Mood and Anxiety All 38/471  0.71/9.78E−06 0.18/4.89E−05 7.73E−05 1.45/8.11E−05 (SASS) CFI-S All 38/470  0.75/1.79E−07  0.2/5.11E−06 1.40E−06 2.02/7.11E−07 Mood and Anxiety and CFI-S All 38/470  0.76/6.34E−08 0.22/4.18E−07 2.22E−06 1.40/1.13E−07 HAMD SI All 35/458* 0.81/5.27E−10 0.40/1.57E−19 2.64E−06 2.10/1.11E−15 Mood and Anxiety and CFI-S All 35/458* 0.82/9.96E−11 0.35/4.11E−15 4.34E−08 1.36/1.83E−13 and HAMD SI Phenes and Biomarkers Mood and Anxiety and CFI-S All 38/470  0.76/6.65E−08 0.21/1.29E−06 2.29E−06 1.37/2.01E−07 and BioM 148 Mood and Anxiety and CFI-S All 38/470  0.77/2.87E−08 0.23/2.81E−07 9.11E−07 1.40/5.31E−08 and BioM 12 (UP-Suicide) Mood and Anxiety and CFI-S All 38/470  0.76/3.87E−08 0.24/1.17E−07 1.02E−06 1.39/3.98E−08 and BioM 2 Mood and Anxiety and CFI-S All 35/458* 0.82/9.38E−11 0.35/3.20E−15 3.39E−08 1.35/1.83E−13 and HAMD SI and BioM 2 Male Bipolars Best Biomarkers PTEN M-BP 4/120  0.9/3.27E−03 0.22/6.76E−03 3.12E−02 1.73/2.73E−02 RNF6 M-BP 4/120 0.82/1.58E−02 −0.14/5.89E−02  9.14E−03 6.24/7.19E−02 BioM 54 Panel M-BP 4/120 0.75/4.23E−02 0.11/1.23E−01 4.71E−02 4.58/2.52E−01 (Bonferroni List) BioM 12 Panel M-BP 4/120 0.56/3.41E−01 0.05/2.85E−01 3.08E−01 2.57/5.73E−01 (Top Dozen List) BioM 2 M-BP 4/120 0.94/1.50E−03 0.23/5.17E−03 3.06E−03 2.68/1.19E−02 (PTEN and RNF6) Phenes Mood M-BP 4/120 0.69/1.04E−01 −0.14/6.08E−02  1.75E−01 2.10/1.32E−01 Anxiety M-BP 4/120 0.70/9.29E−02 0.12/9.74E−02 1.12E−01 1.87/2.09E−02 Mood and Anxiety (SASS) M-BP 4/120 0.72/7.19E−02 0.15/5.27E−02 1.34E−01 1.52/1.18E−01 CFI-S MBP 4/120 0.80/2.10E−02 0.15/5.22E−02 3.46E−03 1.95/1.21E−01 Mood and Anxiety and CFI-S M-BP 4/120 0.78/2.77E−02 0.18/2.36E−02 6.78E−02 1.41/5.54E−02 Phenes and Biomarkers Mood and Anxiety and CFIS M-BP 4/120 0.81/1.64E−02  0.2/1.61E−02 5.13E−02 1.45/4.04E−02 and BioM 54 Mood and Anxiety and CFI-S M-BP 4/120 0.79/2.59E−02 0.19/1.88E−02 7.92E−02 1.44/4.72E−02 and BioM 12 (UP-Suicide Male BP) Mood and Anxiety and CFI-S M-BP 4/120 0.86/7.02E−03 0.25/3.48E−03 2.22E−02 1.55/1.18E−2  and BioM 2

Predicting Future Hospitalizations for Suicidality in First Year Following Testing. Analyses for predicting hospitalizations for suicidality in the first year following each testing visit were conducted in subjects that had at least one year of follow-up in the VA system, for which there was access to complete electronic medical records. ROC analyses between genomic and phenomic marker levels at a specific testing visit and future hospitalizations were performed as described above, based on assigning if participants had been hospitalized for suicidality (ideation, attempts) or not within one year following a testing visit. Additionally, a one tailed t-test with unequal variance was performed between groups of participant visits with and without future hospitalizations for suicidality. Pearson R (one-tail) correlation was performed between hospitalization frequency (number of hospitalizations for suicidality divided by duration of follow-up) and marker levels.

A correlation analyses for hospitalization frequency for all future hospitalizations due to suicidality was also conducted, including those occurring beyond one year of follow-up, in the years following testing (on average 4.90 years per participant, range 0.40 to 10.42 years), as this calculation, unlike the ROC and t-test, accounts for the actual length of follow-up, which varied from participant to participant. The ROC and t-test might in fact, if used, under-represent the power of the markers to predict, as the more severe psychiatric patients are more likely to move geographically and/or be lost to follow-up.

Therapeutics

The individual top biomarkers known to be modulated by existing drugs were analyzed using the CFG databases, and using Ingenuity Drugs analyses (Tables 5A-5G). Drugs and natural compounds which are an opposite match for the gene expression profile of panels of the top biomarkers (top dozen biomarkers, Bonferroni corrected) were also analyzed using the Connectivity Map (Broad Institute, MIT) (Tables 6-18). For the top dozen universal biomarker panel, 7 of 12 probesets were present of the array used for the Connectivity Map; for the Bonferroni universal biomarker panel, 102 out of 148 probesets; for the top dozen male bipolar panel, 8 out of 12 probesets; and for the Bonferroni male bipolar panel, 31 out of 56 probesets.

TABLE 5 (Direction of Change in Suicidality) Analysis/ Modulated Modulated Modulated Modulated Modulated by Modulated Modulated Gene Symbol Internal by by by by other other Mood by other by other Gene Name Score Omega-3 Lithium Clozapine Antidepressants Stabilizers Antipsychotics Drugs A. Top Universal Biomarkers for Suicidality - Pharmacogenomics for potential stratification and monitoring response to treatment. Biomarker genes that are targets of existing drugs and modulated by them in opposite direction to suicide. CCL28 (D) Paroxetine chemokine AP/4 (C-C motif) ligand 28 HTR2A (I) Yes Buspirone, Valproate Haloperidol 5- DE/2 mirtazapine, Paliperidone, hydroxytryptamine amitriptyline Risperidone, (serotonin) Iloperidone, receptor 2A, G asenapine, protein-coupled cariprazine, thioproperazine, lurasidone, opipramol, quetiapine, olanzapine, IFNG (D) Olanzapine, interferon, AP/1 Risperidone, gamma Quetiapine, Aripiprazole ITGB1BP1 (D) Yes integrin beta 1 DE/1 binding protein 1 LHFP (I) Yes lipoma HMGIC DE/1 fusion partner PTK2 (I) CT-707 protein tyrosine DE/1 kinase 2 SLC4A4 (I) Valproate solute carrier AP/1 family 4 (sodium bicarbonate cotransporter), member 4 Direction of Change in Suicidality Analysis/ Modulated Modulated Modulated Modulated Modulated by Modulated Modulated Gene Symbol Internal by by by by other other Mood by other by other Gene Name Score Omega-3 Lithium Clozapine Antidepressants Stabilizers Antipsychotics Drugs B. Top Biomarkers for Suicidality in Males - Pharmacogenomics for potential stratification and monitoring response to treatment. Biomarker genes that are targets of existing drugs and modulated by them in opposite direction to suicide AGT I Yes Angiotensinogen AP/1 GDI2 D Yes Benzodiazepines GDP Dissociation DE/1 Inhibitor 2 IL6 I Yes Yes Yes tocilizumab, Interleukin 6 AP/2 siltuximab ITGB1BP1 D Yes Integrin Subunit DE/1 Beta 1 Binding Protein 1 PRKACB D Yes Protein Kinase AP/4 CAMP-Activated Catalytic Subunit Beta SAT1 I Yes Spermidine/Spermine DE/1 N1- Acetyltransferase 1 SLC4A4 I Valproate Solute Carrier Family AP/2 4 Member 4 SLC6A4 I Yes Yes bicifadine, Solute Carrier Family DE/2 SSRIs DOV-102,677, 6 Member 4 SNRIs SLV-314 TM4SF1 I Yes Yes Transmembrane 4 L AP/1 Six Family Member 1 ZMYND8 D Yes Zinc Finger MYND- AP/1 Type Containing 8 C. Top Biomarkers for Suicidality in Females - Pharmacogenomics for potential stratification and monitoring response to treatment. Biomarker genes that are targets of existing drugs and modulated by them in opposite direction to suicide BDNF I Yes Fluoxetine Haloperidol Mifepristone Brain Derived DE/2 Neurotrophic Factor HS6ST2 I Yes Heparan Sulfate 6- DE/1 O-Sulfotransferase 2 HTR2A I Yes Yes Buspirone, Valproate Haloperidol 5- DE/2 mirtazapine, Paliperidone, Hydroxytryptamine amitriptyline Risperidone, Receptor 2A Iloperidone, asenapine, cariprazine, thioproperazine, lurasidone, opipramol, quetiapine, olanzapine, IFNG D Yes Yes Interferon Gamma AP/1 NTRK3 I Yes TSR-011, Neurotrophic DE/2 entrectinib, Receptor Tyrosine PLX7486, Kinase 3 DS-6051b TPR D Valproate Translocated AP/4 Promoter Region, Nuclear Basket Protein (Direction of Change in Suicidality) Analysis/ Modulated Modulated Modulated Modulated Modulated by Modulated Modulated Gene Symbol Internal by by by by other other Mood by other by other Gene Name Score Omega-3 Lithium Clozapine Antidepressants Stabilizers Antipsychotics Drugs D. Top Biomarkers for Suicidality in Bipolar Disorder - Pharmacogenomics for potential stratification and monitoring response to treatment. Biomarker genes that are targets of existing drugs and modulated by them in opposite direction to suicide HTR2A (I) Yes Yes Buspirone, Valproate Haloperidol 5- DE/2 mirtazapine, Paliperidone, Hydroxytryptamine amitriptyline Risperidone, Receptor 2A Iloperidone, asenapine, cariprazine, thioproperazine, lurasidone, opipramol, quetiapine, olanzapine, ITPKB (I) Yes Inositol- AP/2 Trisphosphate 3- Kinase B PIK3R1 (I) Yes Phosphoinositide- DE/1 3-Kinase Regulatory Subunit 1 SAT1 (I) Yes Spermidine/Spermine DE/1 N1- Acetyltransferase 1 SLC6A4 (D) Yes Yes Fluoxetine bicifadine, Solute Carrier DE/1 DOV-102,677, Family 6 Member 4 SLV-314 TM4SF1 (I) Yes Yes Transmembrane AP/1 4 L Six Family Member 1 TNF (I) Sertraline , etanercept, Tumor Necrosis DE/1 Venlafaxine infliximab, Factor (I) certolizumab, AP/1 golimumab, thalidomide E. Top Biomarkers for Suicidality in Depression - Pharmacogenomics for potential stratification and monitoring response to treatment. Biomarker genes that are targets of existing drugs and modulated by them in opposite direction to suicide BDNF (I) Yes Fluoxetine Haloperidol Mifepristone Brain Derived DE/1 Neurotrophic Factor DLK1 (I) Yes Delta Like AP/2 Non-Canonical (I) Notch Ligand 1 DE/1 NTRK3 (I) Yes TSR-011, Neurotrophic Receptor AP/2 entrectinib, Tyrosine Kinase 3 (I) PLX7486, DE/1 DS-6051b ACP1 (D) Yes Fluoxetine Olanzapine Acid Phosphatase 1, AP/1 Soluble TSPYL1 (D) Yes Valproate TSPY Like 1 AP/1 CD47 (D) Yes Yes CD47 Molecule AP/2 (D) DE/1 GLIPR1 (D) Valproate GLI Pathogenesis DE/1 Related 1 GEM (I) Yes GTP Binding Protein AP/1 Overexpressed In Skeletal Muscle JUN (I) Yes Yes Fluoxetine Jun Proto-Oncogene, AP-1 DE/1 Transcription Factor Subunit GIMAP4 (D) Benzodiazepines GTPase, IMAP Family DE/4 Member 4 HNRNPL (D) Yes Heterogeneous Nuclear DE/4 Ribonucleoprotein L F. Top Biomarkers for Suicidality in Males with Bipolar Disorder - Pharmacogenomics for potential stratification and monitoring response to treatment. Biomarker genes that are targets of existing drugs and modulated by them in opposite direction to suicide HTR2A (I) Yes Yes Buspirone, Valproate Haloperidol 5-hydroxytryptamine DE/2 mirtazapine, Paliperidone, (serotonin) receptor amitriptyline Risperidone, 2A, G protein-coupled Iloperidone, asenapine, cariprazine, thioproperazine, lurasidone, opipramol, quetiapine, olanzapine, SPTBN1 (I) Yes spectrin, beta, AP/1 non-erythrocytic 1 G. Top Biomarkers for Suicidality in Males with Depression - Pharmacogenomics for potential stratification and monitoring response to treatment. Biomarker genes that are targets of existing drugs and modulated by them in opposite direction to suicide DLK1 (I) Yes Delta Like Non- AP/2 Canonical Notch Ligand 1 NTRK3 (D) Fluoxetine TSR-011, Neurotrophic AP/2 entrectinib, Receptor PLX7486, Tyrosine Kinase 3 DS-6051b CD47 D Yes Yes CD47 Molecule AP/2 PTK2 I CT-707 Protein Tyrosine DE/1 Kinase 2 TSPYL1 D Yes Valproate TSPY Like 1 AP/1 HNRNPL (D) Yes Heterogeneous DE/4 Nuclear Ribonucleoprotein L

TABLE 6 Repurposed Drugs for Suicidality Treatment in Everybody (Universal) compound name dose cell score gene expression signature dapsone 16 μM HL60 −1 Top Predictor Biomarkers ebselen 15 μM PC3 −1 Top Dozen Biomarkers chlorogenic acid 11 μM HL60 −1 Bonferroni Biomarkers clemastine  9 μM HL60 −0.983 Top Predictor Biomarkers metformin 24 μM HL60 −0.983 Bonferroni Biomarkers piracetam 28 μM MCF7 −0.973 Top Dozen Biomarkers dihydroergocristine  6 μM MCF7 −0.946 Top Dozen Biomarkers amoxapine 13 μM MCF7 −0.927 Top Dozen Biomarkers metformin 24 μM HL60 −0.925 Top Predictor Biomarkers lisuride 12 μM PC3 −0.922 Top Dozen Biomarkers homatropine 11 μM HL60 −0.917 Top Predictor Biomarkers ritodrine 12 μM HL60 −0.916 Top Predictor Biomarkers merbromin  5 μM HL60 −0.904 Top Predictor Biomarkers naproxen 16 μM MCF7 −0.903 Top Dozen Biomarkers dl-alpha tocopherol  9 μM HL60 −0.885 Top Predictor Biomarkers chlorpromazine 11 μM HL60 −0.877 Top Predictor Biomarkers diphenhydramine 14 μM HL60 −0.873 Bonferroni Biomarkers genistein 10 μM PC3 −0.869 Top Dozen Biomarkers fluoxetine 12 μM HL60 −0.851 Top Predictor Biomarkers adiphenine 11 μM HL60 −0.847 Top Predictor Biomarkers chlorogenic acid 11 μM HL60 −0.842 Top Predictor Biomarkers yohimbine 10 μM MCF7 −0.842 Top Predictor Biomarkers prazosin 10 μM PC3 −0.838 Top Predictor Biomarkers amitriptyline 13 μM HL60 −0.827 Top Predictor Biomarkers calcium folinate  8 μM MCF7 −0.825 Bonferroni Biomarkers Using Universal Biomarker Signatures, as identified herein, Matching to the Connectivity Map (Cmap) to identify compounds that have opposite gene expression effects to suicide. A score of −1 means perfect opposite effect. Bold—known antidepressant/psychotropic. Italic—natural compound

TABLE 7 Repurposed Drugs for Suicidality Treatment in Males compound name dose cell score gene expression signature clemastine 9 μM HL60 −1 Top Predictor Biomarkers metformin 24 μM HL60 −1 Bonferroni Biomarkers chlorpromazine 11 μM HL60 −0.997 Top Predictor Biomarkers thiamine 12 μM MCF7 −0.989 Top Dozen Biomarkers hydrochlorothiazide 13 μM MCF7 −0.984 Top Dozen Biomarkers LY-294002 100 nM MCF7 −0.981 Top Predictor Biomarkers naringin 7 μM MCF7 −0.963 Top Dozen Biomarkers betulin 9 μM HL60 −0.952 Top Dozen Biomarkers ritodrine 12 μM HL60 −0.941 Top Predictor Biomarkers fluvastatin 9 μM PC3 −0.935 Top Predictor Biomarkers dapsone 16 μM HL60 −0.913 Top Predictor Biomarkers ranitidine 11 μM MCF7 −0.908 Top Dozen Biomarkers diphenhydramine 14 μM MCF7 −0.906 Top Dozen Biomarkers mephenesin 22 μM MCF7 −0.905 Top Predictor Biomarkers thiamphenicol 11 μM HL60 −0.904 Top Predictor Biomarkers dizocilpine 12 μM MCF7 −0.9 Top Predictor Biomarkers metformin 24 μM HL60 −0.885 Top Predictor Biomarkers droperidol 11 μM HL60 −0.85 Top Predictor Biomarkers lisuride 12 μM MCF7 −0.85 Top Predictor Biomarkers vitexin 9 μM PC3 −0.842 Top Predictor Biomarkers risperidone 10 μM MCF7 −0.841 Top Predictor Biomarkers fluoxetine 12 μM HL60 −0.831 Bonferroni Biomarkers Using the identified Male Biomarker Signatures Matching to the Connectivity Map (Cmap) to identify compounds that have opposite gene expression effects to suicide. A score of −1 means perfect opposite effect. Bold—known antidepressant/psychotropic. Italic—natural compound

TABLE 8 Repurposed Drugs for Suicidality Treatment in Females compound name dose cell score gene expression signature estradiol 100 nM HL60 −1 Bonferroni Biomarkers pizotifen 9 μM HL60 −1 Top Dozen Biomarkers rosiglitazone 10 μM HL60 −1 Top Dozen Biomarkers orlistat 10 μM MCF7 −0.972 Top Dozen Biomarkers nefopam 14 μM MCF7 −0.953 Bonferroni Biomarkers biperiden 11 μM MCF7 −0.941 Bonferroni Biomarkers fluoxetine 12 μM HL60 −0.927 Bonferroni Biomarkers cyanocobalamin 3 μM MCF7 −0.896 Top Dozen Biomarkers vitexin 9 μM MCF7 −0.895 Top Dozen Biomarkers hesperetin 13 μM PC3 −0.883 Top Dozen Biomarkers kawain 17 μM MCF7 −0.883 Bonferroni Biomarkers ergocalciferol 10 μM HL60 −0.832 Bonferroni Biomarkers Using the identified Female Biomarkers Signatures Matching to the Connectivity Map (Cmap) to identify compounds that have opposite gene expression effects to suicide. A score of −1 means perfect opposite effect. Bold—known antidepressant/psychotropic. Italic—natural compound

TABLE 9 Repurposed Drugs for Suicidality Treatment in Bipolar Disorder compound name dose cell score gene expression signature phenelzine 17 μM MCF7 −1 Top Predictor Biomarkers methocarbamol 17 μM PC3 −1 Top Dozen Biomarkers baclofen 19 μM PC3 −1 Bonferroni Biomarkers mepenzolate bromide 10 μM PC3 −0.993 Top Predictor Biomarkers lobelanidine 11 μM MCF7 −0.992 Top Predictor Biomarkers genistein 10 μM MCF7 −0.985 Top Dozen Biomarkers lactobionic acid 11 μM MCF7 −0.974 Top Dozen Biomarkers fluocinonide 8 μM PC3 −0.968 Top Predictor Biomarkers apigenin 15 μM PC3 −0.957 Top Predictor Biomarkers betahistine 17 μM MCF7 −0.948 Top Dozen Biomarkers levonorgestrel 13 μM PC3 −0.933 Top Predictor Biomarkers amoxapine 13 μM PC3 −0.932 Top Dozen Biomarkers (+/−)-catechin 14 μM MCF7 −0.931 Top Predictor Biomarkers apigenin 15 μM PC3 −0.93 Bonferroni Biomarkers fenoprofen 7 μM PC3 −0.923 Top Predictor Biomarkers carisoprodol 15 μM MCF7 −0.919 Bonferroni Biomarkers benfotiamine 9 μM PC3 −0.918 Bonferroni Biomarkers felodipine 10 μM MCF7 −0.917 Bonferroni Biomarkers nifedipine 12 μM MCF7 −0.914 Bonferroni Biomarkers 0175029-0000 10 μM PC3 −0.913 Top Predictor Biomarkers nifuroxazide 15 μM HL60 −0.91 Top Predictor Biomarkers cotinine 23 μM MCF7 −0.862 Top Dozen Biomarkers ergocalciferol 10 μM MCF7 −0.86 Top Dozen Biomarkers resveratrol 18 μM MCF7 −0.857 Top Predictor Biomarkers hesperetin 13 μM PC3 −0.854 Top Dozen Biomarkers Using the identified Bipolar Biomarkers Signatures Matching to the Connectivity Map (Cmap) to identify compounds that have opposite gene expression effects to suicide. A score of −1 means perfect opposite effect. Bold—known antidepressant/psychotropic. Italic—natural compound

TABLE 10 Repurposed Drugs for Suicidality Treatment in Depression compound name dose  cell score gene expression signature hyoscyamine 14 μM  HL60 −1 Top Dozen Biomarkers metrizamide 5 μM  HL60 −1 Top Dozen Biomarkers nadolol 13 μM  MCF7 −1 Bonferroni Biomarkers mebhydrolin 5 μM  HL60 −0.969 Top Dozen Biomarkers rofecoxib 10 μM  MCF7 −0.966 Top Dozen Biomarkers gabapentin 23 μM  MCF7 −0.958 Top Dozen Biomarkers thiamazole 35 μM  MCF7 −0.953 Top Dozen Biomarkers celecoxib 10 μM  MCF7 −0.952 Top Dozen Biomarkers nimodipine 10 μM  MCF7 −0.951 Bonferroni Biomarkers estradiol 10 nM  MCF7 −0.949 Top Dozen Biomarkers ginkgolide A 10 μM  PC3 −0.946 Top Dozen Biomarkers harmine 16 μM  HL60 −0.931 Top Dozen Biomarkers nifedipine 12 μM  PC3 −0.929 Top Dozen Biomarkers SC-58125 10 μM  MCF7 −0.929 Top Dozen Biomarkers noscapine 10 μM  MCF7 −0.924 Top Dozen Biomarkers thiamine 12 μM  MCF7 −0.922 Top Dozen Biomarkers diphenhydramine 14 μM  HL60 −0.861 Bonferroni Biomarkers metformin 24 μM  HL60 −0.84 Bonferroni Biomarkers Using the identified Depression Biomarkers Signatures Matching to the Connectivity Map (Cmap) to identify compounds that have opposite gene expression effects to suicide. A score of −1 means perfect opposite effect. Bold - known antidepressant/psychotropic. Italic - natural compound

TABLE 11 Repurposed Drugs for Suicidality Treatment in Males with Bipolar Disorder compound name dose cell score gene expression signature betonicine 25 μM MCF7 −1 Top Predictor Biomarkers betulin 9 μM HL60 −1 Top Dozen Biomarkers Prestwick-692 7 μM MCF7 −1 Top Dozen Biomarkers chlorphenesin 16 μM HL60 −1 Bonferroni Biomarkers naproxen 16 μM PC3 −0.96 Bonferroni Biomarkers biperiden 11 μM PC3 −0.948 Top Dozen Biomarkers carteolol 12 μM HL60 −0.946 Top Dozen Biomarkers baclofen 19 μM PC3 −0.94 Bonferroni Biomarkers harmaline 14 μM MCF7 −0.932 Top Dozen Biomarkers carteolol 12 μM HL60 −0.907 Top Dozen Biomarkers amylocaine 15 μM MCF7 −0.9 Top Predictor Biomarkers estradiol 10 nM MCF7 −0.894 Top Dozen Biomarkers acacetin 14 μM PC3 −0.882 Bonferroni Biomarkers alpha-ergocryptine 7 μM MCF7 −0.862 Bonferroni Biomarkers myosmine 27 μM MCF7 −0.846 Top Predictor Biomarkers zuclopenthixol 9 μM MCF7 −0.839 Top Predictor Biomarkers benfotiamine 9 μM PC3 −0.839 Bonferroni Biomarkers valproic acid 500 μM PC3 −0.832 Top Predictor Biomarkers resveratrol 18 μM HL60 −0.826 Top Dozen Biomarkers azacyclonol 15 μM MCF7 −0.814 Top Predictor Biomarkers allantoin 25 μM PC3 −0.811 Top Dozen Biomarkers Using the identified Bipolar Males Biomarker Signatures Matching to the Connectivity Map (Cmap) to identify compounds that have opposite gene expression effects to suicide. A score of −1 means perfect opposite effect. Bold - known antidepressant/psychotropic. Italic - natural compound

TABLE 12 Repurposed Drugs for Suicidality Treatment in Males with Depression compound name dose cell score gene expression signature suloctidil 12 μM  PC3 −1 Top Predictor Biomarkers vincamine 11 μM  MCF7 −1 Top Dozen Biomarkers ciprofibrate 14 μM  HL60 −1 Bonferroni Biomarkers methanthelinium bromide 10 μM  HL60 −0.996 Bonferroni Biomarkers amantadine 10 μM  MCF7 −0.967 Bonferroni Biomarkers estradiol 10 nM  ssMCF7 −0.956 Top Dozen Biomarkers fenspiride 13 μM  PC3 −0.945 Top Dozen Biomarkers nimodipine 10 μM  PC3 −0.939 Top Dozen Biomarkers lansoprazole 11 μM  HL60 −0.931 Bonferroni Biomarkers famotidine 12 μM  MCF7 −0.923 Top Dozen Biomarkers cyclopenthiazide 11 μM  HL60 −0.917 Top Predictor Biomarkers cyclopenthiazide 11 μM  HL60 −0.91 Top Dozen Biomarkers fluvoxamine 9 μM  MCF7 −0.903 Top Dozen Biomarkers adipiodone 4 μM  HL60 −0.902 Top Predictor Biomarkers calcium folinate 8 μM  HL60 −0.902 Bonferroni Biomarkers trichostatin A 1 μM  MCF7 −0.892 Top Predictor Biomarkers docosahexaenoic acid ethyl ester 100 μM  PC3 −0.889 Top Dozen Biomarkers metformin 10 μM  MCF7 −0.882 Top Dozen Biomarkers calcium folinate 8 μM  HL60 −0.869 Top Predictor Biomarkers chlorogenic acid 11 μM  HL60 −0.864 Bonferroni Biomarkers dosulepin 12 μM  HL60 −0.831 Top Predictor Biomarkers thioproperazine 6 μM  HL60 −0.831 Top Predictor Biomarkers rolipram 15 μM  PC3 −0.811 Top Predictor Biomarkers citalopram 1 μM  MCF7 −0.787 Top Predictor Biomarkers Using Our Depression Males Biomarker Signatures Matching to the Connectivity Map (Cmap) to identify compounds that have opposite gene expression effects to suicide. A score of −1 means perfect opposite effect. Bold - known antidepressant/psychotropic. Italic - natural compound

TABLE 13 Repurposed Drugs for Suicidality Treatment in Males with Post-Traumatic Stress Disorder (PTSD) compound name dose cell score gene expression signature hemicholinium 7 μM PC3 −1 Top Predictor Biomarkers epitiostanol 13 μM PC3 −0.974 Top Predictor Biomarkers pirenperone 10 μM HL60 −0.913 Top Predictor Biomarkers tretinoin 13 μM PC3 −0.901 Top Predictor Biomarkers betamethasone 10 μM PC3 −0.901 Top Predictor Biomarkers tolnaftate 13 μM MCF7 −0.895 Top Predictor Biomarkers atractyloside 5 μM HL60 −0.884 Top Predictor Biomarkers prochlorperazine 7 μM HL60 −0.878 Top Predictor Biomarkers tolazoline 20 μM MCF7 −0.866 Top Predictor Biomarkers fulvestrant 10 nM HL60 −0.858 Top Predictor Biomarkers procainamide 15 μM HL60 −0.844 Top Predictor Biomarkers pioglitazone 10 μM PC3 −0.839 Top Predictor Biomarkers calcium folinate 8 μM MCF7 −0.838 Top Predictor Biomarkers merbromin 5 μM HL60 −0.831 Top Predictor Biomarkers adipiodone 4 μM HL60 −0.831 Top Predictor Biomarkers benzbromarone 9 μM HL60 −0.83 Top Predictor Biomarkers prazosin 10 μM PC3 −0.828 Top Predictor Biomarkers Using the identified PTSD Males Biomarker Signatures Matching to the Connectivity Map (Cmap) to identify compounds that have opposite gene expression effects to suicide. A score of −1 means perfect opposite effect. Bold - known antidepressant/psychotropic. Italic - natural compound

TABLE 14 Repurposed Drugs for Suicidality Treatment in Males with Schizophrenia and Schizoaffective Disorder (SZ/SZA) compound name dose cell score gene expression signature asiaticoside 4 μM HL60 −1 Top Predictor Biomarkers procainamide 15 μM HL60 −0.959 Top Predictor Biomarkers 3-hydroxy-DL-kynurenine 18 μM HL60 −0.946 Top Predictor Biomarkers mafenide 18 μM HL60 −0.913 Top Predictor Biomarkers metformin 24 μM HL60 −0.899 Top Predictor Biomarkers trimipramine 10 μM HL60 −0.895 Top Predictor Biomarkers ramifenazone 14 μM HL60 −0.885 Top Predictor Biomarkers lithocholic acid 11 μM HL60 −0.881 Top Predictor Biomarkers chlorogenic acid 11 μM HL60 −0.878 Top Predictor Biomarkers hydrastinine 16 μM HL60 −0.875 Top Predictor Biomarkers diphenhydramine 14 μM HL60 −0.874 Top Predictor Biomarkers clozapine 12 μM HL60 −0.868 Top Predictor Biomarkers Using the identified SZ/SZA Males Biomarker Signatures Matching to the Connectivity Map (Cmap) to identify compounds that have opposite gene expression effects to suicide. A score of −1 means perfect opposite effect. Bold - known antidepressant/psychotropic. Italic - natural compound

TABLE 15 Repurposed Drugs for Suicidality Treatment in the High Anxiety Subtype compound name dose cell score gene expression signature ethaverine 9 μM PC3 −1 Top Predictor Biomarkers moracizine 9 μM HL60 −0.969 Top Predictor Biomarkers dl-alpha tocopherol 9 μM HL60 −0.944 Top Predictor Biomarkers cefalotin 10 μM PC3 −0.933 Top Predictor Biomarkers calcium folinate 8 μM PC3 −0.855 Top Predictor Biomarkers indoprofen 14 μM PC3 −0.854 Top Predictor Biomarkers ethoxyquin 18 μM PC3 −0.825 Top Predictor Biomarkers mesalazine 26 μM MCF7 −0.824 Top Predictor Biomarkers valproic acid 500 μM MCF7 −0.822 Top Predictor Biomarkers orphenadrine 13 μM PC3 −0.82 Top Predictor Biomarkers thioridazine 10 μM HL60 −0.819 Top Predictor Biomarkers risperidone 10 μM HL60 −0.812 Top Predictor Biomarkers trifluoperazine 10 μM HL60 −0.811 Top Predictor Biomarkers thioproperazine 6 μM PC3 −0.804 Top Predictor Biomarkers chlorpromazine 11 μM HL60 −0.791 Top Predictor Biomarkers Using the Top Predictor Biomarker Signatures Matching to the Connectivity Map (Cmap) to identify compounds that have opposite gene expression effects to suicide. A score of −1 means perfect opposite effect. Bold - known antidepressant/psychotropic. Italic - natural compound

TABLE 16 Repurposed Drugs for Suicidality Treatment in the Low Mood Subtype compound name dose cell score gene expression signature streptomycin 3 μM MCF7 −1 Top Predictor Biomarkers isoetarine 12 μM MCF7 −0.988 Top Predictor Biomarkers carbimazole 21 μM HL60 −0.947 Top Predictor Biomarkers IC-86621 1 μM PC3 −0.944 Top Predictor Biomarkers dapsone 16 μM HL60 −0.94 Top Predictor Biomarkers bumetanide 11 μM MCF7 −0.909 Top Predictor Biomarkers pergolide 10 μM PC3 −0.906 Top Predictor Biomarkers sulindac 11 μM PC3 −0.905 Top Predictor Biomarkers bemegride 26 μM MCF7 −0.904 Top Predictor Biomarkers yohimbine 10 μM MCF7 −0.894 Top Predictor Biomarkers cotinine 23 μM MCF7 −0.892 Top Predictor Biomarkers prochlorperazine 7 μM HL60 −0.891 Top Predictor Biomarkers chlorprothixene 11 μM MCF7 −0.885 Top Predictor Biomarkers sulindac 11 μM PC3 −0.88 Top Predictor Biomarkers ramifenazone 14 μM HL60 −0.874 Top Predictor Biomarkers boldine 12 μM HL60 −0.874 Top Predictor Biomarkers dl-alpha tocopherol 9 μM HL60 −0.87 Top Predictor Biomarkers nordihydroguaiaretic acid 1 μM ssMCF7 −0.858 Top Predictor Biomarkers serotonin 19 μM PC3 −0.854 Top Predictor Biomarkers diphenhydramine 14 μM HL60 −0.852 Top Predictor Biomarkers Using the Top Predictor Biomarker Signatures Matching to the Connectivity Map (Cmap) to identify compounds that have opposite gene expression effects to suicide. A score of −1 means perfect opposite effect. Bold - known antidepressant/psychotropic. Italic - natural compound

TABLE 17 Repurposed Drugs for Suicidality Treatment in the High Psychosis (Non-Affective) Subtype compound name dose cell score gene expression signature PF-01378883-00 10 μM MCF7 −0.975 Top Predictor Biomarkers ketotifen 9 μM MCF7 −0.959 Top Predictor Biomarkers levamisole 17 μM MCF7 −0.938 Top Predictor Biomarkers tenoxicam 12 μM HL60 −0.934 Top Predictor Biomarkers ifosfamide 15 μM MCF7 −0.933 Top Predictor Biomarkers naloxone 11 μM MCF7 −0.931 Top Predictor Biomarkers timolol 9 μM MCF7 −0.928 Top Predictor Biomarkers metformin 24 μM HL60 −0.926 Top Predictor Biomarkers iocetamic acid 7 μM HL60 −0.922 Top Predictor Biomarkers rofecoxib 10 μM MCF7 −0.921 Top Predictor Biomarkers pepstatin 6 μM MCF7 −0.913 Top Predictor Biomarkers isocarboxazid 17 μM PC3 −0.909 Top Predictor Biomarkers tinidazole 16 μM MCF7 −0.908 Top Predictor Biomarkers mefexamide 13 μM PC3 −0.907 Top Predictor Biomarkers etodolac 14 μM MCF7 −0.907 Top Predictor Biomarkers myricetin 13 μM MCF7 −0.899 Top Predictor Biomarkers promazine 12 μM MCF7 −0.897 Top Predictor Biomarkers nomegestrol 11 μM MCF7 −0.884 Top Predictor Biomarkers lobelanidine 11 μM MCF7 −0.881 Top Predictor Biomarkers diphenhydramine 14 μM HL60 −0.878 Top Predictor Biomarkers Using the Top Predictor Biomarker Signatures Matching to the Connectivity Map (Cmap) to identify compounds that have opposite gene expression effects to suicide. A score of −1 means perfect opposite effect. Bold - known antidepressant/psychotropic. Italic - natural compound

TABLE 18 Repurposed Drugs for Suicidality Treatment in the Combined (Affective) Subtype compound name dose  cell score gene expression signature trimipramine 10 μM  HL60 −1 Top Predictor Biomarkers proguanil 14 μM  HL60 −1 Top Predictor Biomarkers cyclopenthiazide 11 μM  HL60 −0.961 Top Predictor Biomarkers lansoprazole 11 μM  HL60 −0.941 Top Predictor Biomarkers ozagrel 15 μM  HL60 −0.939 Top Predictor Biomarkers asiaticoside 4 μM  HL60 −0.928 Top Predictor Biomarkers metformin 24 μM  HL60 −0.92 Top Predictor Biomarkers corticosterone 12 μM  HL60 −0.907 Top Predictor Biomarkers chlorogenic acid 11 μM  HL60 −0.904 Top Predictor Biomarkers ondansetron 12 μM  HL60 −0.876 Top Predictor Biomarkers betulin 9 μM  HL60 −0.875 Top Predictor Biomarkers pirenperone 10 μM  HL60 −0.872 Top Predictor Biomarkers adiphenine 11 μM  HL60 −0.855 Top Predictor Biomarkers felbinac 19 μM  MCF7 −0.853 Top Predictor Biomarkers finasteride 11 μM  HL60 −0.843 Top Predictor Biomarkers rilmenidine 8 μM  HL60 −0.833 Top Predictor Biomarkers ritodrine 12 μM  HL60 −0.826 Top Predictor Biomarkers dexamethasone 9 μM  PC3 −0.819 Top Predictor Biomarkers cyclic adenosine monophosphate 12 μM  HL60 −0.806 Top Predictor Biomarkers fluoxetine 12 μM  HL60 −0.805 Top Predictor Biomarkers Using the Top Predictor Biomarker Signatures Matching to the Connectivity Map (Cmap) to identify compounds that have opposite gene expression effects to suicide. A score of −1 means perfect opposite effect. Bold - known antidepressant/psychotropic. Italic - natural compound Understanding

Pathway Analyses

IPA (Ingenuity Pathway Analyses, version 24390178, Qiagen), David Functional Annotation Bioinformatics Microarray Analysis (National Institute of Allergy and Infectious Diseases), and Kyoto Encyclopedia of Genes and Genomes (KEGG) (through DAVID) were used to analyze the biological roles, including top canonical pathways, and diseases, of the candidate genes, as well as to identify genes in that dataset that are the targets of existing drugs (Table 19). The pathway analyses were conducted for the combined AP and DE probesets with a total internal and external CFG prioritization score >4 that showed stepwise change in the suicide completers validation cohort and survived Bonferroni correction (130 genes, 148 probesets) (Table 4). For male bipolars, there were 50 genes, 54 probesets.

TABLE 19 Biological Pathways and Diseases A. Universal biomarkers Universal DAVID GO Functional Annotation Biological Processes KEGG Pathways Pathways # Term Count % P-Value Term Count % P-Value Validation 1 Regulation of 8 6.6 2.10E−04 Tryptophan 4 0.2 1.10E−02 Bonferroni neurogenesis metabolism Significant 2 Negative regulation 11 9 2.60E−04 Neurotrophin 6 0.3 1.40E−02 in Suicide of apoptosis signaling Completers pathway (n = 130 3 Negative regulation 11 9 2.90E−04 Insulin 6 0.3 1.90E−02 genes) of programmed signaling cell death pathway 4 Negative regulation 11 9 3.00E−04 Butanoate 3 0.2 5.90E−02 of cell death metabolism 5 Regulation of cell 7 5.7 3.90E−04 Endocytosis 6 0.3 6.10E−02 morphogenesis Ingenuity Pathways Top Universal Canonical Pathways Pathways P-Value Overlap Validation Protein 4.36E−06 0.03112/386  Bonferroni Kinase A Significant Signaling in Suicide IGF-1 2.86E−05 0.06235/582  Completers Signaling (n = 130 Gap 4.66E−05 0.0457/155 genes) Junction Signaling Renin- 5.52E−05 0.0556/109 Angiotensin Signaling Hepatic 5.93E−05 0.0437/161 Cholestasis Ingenuity Universal DAVID Diseases and Diseases Term Count % P-Value Disorders P-Value # Molecules Validation 1 diabetes, type 1 9 7.4 1.40E−03 1 Infectious 1.01E−03-1.31E−07 35 Bonferroni Diseases Significant 2 breast cancer 9 7.4 1.40E−02 2 Organismal 1.66E−03-7.72E−07 89 in Suicide Injury and Completers Abnormalities (n = 130 3 hypertension 7 5.7 1.60E−02 3 Developmental 1.10E−03-9.64E−07 28 genes) Disorder 4 oxidized LDL 2 1.6 2.30E−02 4 Cancer 1.66E−03-1.38E−06 83 5 brain aging 2 1.6 2.30E−02 5 Cardiovascular 1.66E−03-1.70E−06 18 Disease B. Male Bipolar biomarkers Male Bipolar DAVID GO Functional Annotation Biological Processes KEGG Pathways Pathways # Term Count % P-Value Term Count % P-Value Validation 1 negative regulation 7 14.6 9.30E−06 mTOR signaling 3 6.2 1.60E−02 Bonferroni of neuron pathway significant differentiation in Suicide 2 negative regulation 7 14.6 3.60E−05 Small cell lung 3 6.2 3.20E−02 Completers of neurogenesis cancer (n = 50 3 negative regulation 7 14.6 5.50E−05 Leukocyte 3 6.2 5.80E−02 genes) of nervous system transendothelial development migration 4 positive regulation 4 8.3 1.10E−04 Sphingolipid 3 6.2 6.00E−02 of protein signaling localization to pathway plasma membrane 5 positive regulation 4 8.3 1.10E−04 NA NA NA NA of protein localization to cell periphery B. Male Bipolar biomarkers Ingenuity Pathways Male Top Bipolar Canonical P- Pathways Pathways Value Overlap Validation G-Protein 1.14E−14 0.11329/256 Bonferroni Coupled significant Receptor in Suicide Signaling Completers CREB 1.98E−14  0.1424/171 (n = 50 Signaling genes) in Neurons Neuropathic 4.82E−13  0.1818/100 Pain Signaling In Dorsal Horn Neurons 14-3-3- 7.79E−12 0.15418/117 mediated Signaling Gap Junction 1.50E−11 0.12920/155 Signaling Male Ingenuity Bipolar DAVID Diseases and Diseases # Term Count % P-Value # Disorders P-Value # Molecules Validation 1 plasma HDL 5 10.4 4.80E−03 1 Cancer 6.89E−03-1.18E−05 46 Bonferroni cholesterol significant (HDL-C) levels in Suicide 2 Type 2 Diabetes | 13 27.1 1.30E−02 2 Gastrointestinal 6.89E−03-1.18E−05 41 Completers edema | Disease (n = 50 rosiglitazone genes) 3 Eczema 2 4.2 2.70E−02 3 Organismal Injury 6.89E−03-1.18E−05 46 and Abnormalities 4 Neoplasms 3 6.2 6.00E−02 4 Reproductive 6.89E−03-1.57E−05 20 System Disease 5 healthy oldest-old 2 4.2 6.50E−02 5 Hematological 5.30E−03-2.55E−05 20 Disease

STRING Analysis

In order to examine potential network interactions between the biomarkers, the Search Tool for the Retrieval of Interacting Genes (STRING v10, string-db.org) was used. To run the analyses, the lists of genes were entered into the search box and Homo Sapiens was selected as the organism. The default (medium confidence) setting was used. (FIGS. 8 & 9 ).

CFG Beyond Suicide

A CFG approach was also used to examine evidence from other psychiatric and related disorders, for the top dozen biomarker genes and Bonferroni validated biomarker genes.

Clock Gene Database

For informational non-CFG scoring purposes, the suicide biomarker genes for involvement in the circadian clock were annotated. A database of genes associated with circadian function were compiled by using a combination of review papers (Zhang et al. 2009, McCarthy and Welsh 20129, 10) and searches of existing databases CircaDB (circadb.hogeneschlab.org), GeneCards (www.genecards.org), and GenAtlas (genatlas.medecine.univ-paris5.fr). Using the data compiled from these sources, a total of 1468 genes were identified that show circadian functioning. Genes were further classified into “core” clock genes, i.e., those genes that are the main engine driving circadian function (n=18), “immediate” clock genes, i.e., the genes that directly input or output to the core clock (n=331), and “distant” clock genes, i.e., genes that directly input or output to the immediate clock genes (n=1,119).

Convergent Functional Evidence (CFE)

A convergent functional evidence (CFE) score tabulated all the evidence from discovery (up to 4 points), prioritization (up to 8 points), validation (up to 4 points), testing (2 points for SI predictions, 2 points for hospitalizations predictions), other psychiatric and related disorders (2 points), and drug evidence (2 points). The goal was to highlight, based on the totality of the data and of the evidence in the field to date, biomarkers that have all around evidence: track suicidality, predict suicidality, are reflective of psychiatric pathology, and are potential drug targets. Such biomarkers merit priority evaluation in future clinical trials.

Additionally, a convergent functional evidence (CFE) score was computed with all the evidence from discovery (up to 4 points), prioritization (up to 8 points), testing (High Suicide State and Trait Suicide Hospitalization Future (up to 4 points each if significantly predicts in all subjects, 2 points if predicts by gender, 1 points if predicts in gender/diagnosis subgroups). The goal was to highlight, based on the totality of the data and of the evidence in the field to date, biomarkers that have all-around evidence for tracking suicidality in discovery and validation steps, as well as to permit an objective assessment of state, and predict future clinical events (hospitalizations for suicidality) in the clinical utility testing step.

Results

From Universal to Subtypes and Personalized

Discovery

A powerful within-participant discovery approach to identify genes that: 1. change in expression in blood between no suicidal ideation (no SI) and high suicidal ideation (high SI) states, 2. track the SI state across visits in a participant, and 3. track the SI state in multiple participants. A longitudinally followed cohort of participants was used that showed diametric changes in SI between at least two testing visits (n=66 participants out of a cohort of 293 men and women psychiatric disorder participants followed longitudinally, with diagnoses of bipolar disorder, depression, mood disorder nos, schizophrenia, schizoaffective disorder, psychosis nos, and PTSD). Using a 33% of maximum raw score threshold (internal score of 1 pt), 10,468 unique probesets from AP and DE were found. (FIG. 1D). These were carried forward to the prioritization step. This represents approximately a 5-fold enrichment of the 54,625 probesets on the Affymetrix array.

It was then examined in the discovery cohort whether subtypes of suicidality can be identified based on mental state at the time of high suicidal ideation visits, using two way hierarchical clustering with anxiety, mood, and psychosis measures. The SI state self-report may be more reliable in this cohort, as the subjects demonstrated the aptitude and willingness to report different, and diametric, SI states. Four potential subtypes of suicidality were found: high anxiety, low mood, co-morbid, and non-affective (psychotic) (FIG. 1C). These subtypes need to be tested in independent cohorts for practical utility, diagnostic and therapeutic.

Prioritization

A Convergent Functional Genomics (CFG) approach was used to prioritize the candidate biomarkers identified in the discovery step (internal score of >=1 pt.) by using all of the published prior independent evidence in the field (FIG. 1E). There were 583 probesets that had a CFG score (combined internal and external score) of 4 and above. These were carried forward to the validation step. This represents approximately a 100-fold enrichment of the probesets on the Affymetrix array.

Validation

Next, suicidal behavior was validated for these prioritized biomarkers in a demographically matched cohort of men and women suicide completers from the coroner's office (n=45), by assessing which markers were stepwise changed in expression from no SI to high SI to suicide completers (FIG. 1G). 274 probesets were non-stepwise changed, and 309 were stepwise changed. Of these, 148 survived Bonferroni correction for all the 583 probesets validated. This represents approximately a 500-fold enrichment of the probesets on the Affymetrix array.

Diagnostics

Diagnostic ability of the “universal” top dozen biomarkers (composed of the top increased and decreased biomarkers from AP and from DE from each step: discovery based on all participants, prioritization, and validation in all the coroner's cases) was tested, as well as all of the biomarkers that survived Bonferroni correction after the validation step (Table 3), in a completely independent test cohort of men and women psychiatric disorder participants (n=226), for prediction of suicidal ideation state, as well as for prediction of future psychiatric hospitalizations due to suicidality (FIGS. 3A-3D). Universal biomarkers that work across gender and diagnoses were successfully identified. Their predictive ability was also analyzed in participants in the independent cohort grouped by the subtypes described above, as well as grouped by a more personalized approach, by psychiatric diagnosis and gender. The universal approach was compared to the subtypes approach and the personalized approach, and it was shown that the subtype and personalized approaches permitted enhanced precision of predictions for different biomarkers (FIGS. 3A-3D). For example, for suicidal ideation prediction in the independent test cohort, SLC4A4, a top increased in expression biomarker, had an AUC of 64% (p=3.83E-04) across all subjects, 69% (6.13E-04) in the combined subtype, and 77% (9.72E-04) in male bipolars. SKA2, a top decreased in expression biomarker, had an AUC of 61% (p=3.35E-03) across all subjects, 74% (5.91E-03) in the low mood subtype, and 79% (1.35E-02) in male schizophrenics.

Additionally, two previously described clinical instruments in the form of apps, the Simplified Affective State Scale (SASS) that measures anxiety and mood, and the Convergent Functional Information for Suicidality (CFI-S) that measures risk for suicide indirectly, were used without asking about suicidal ideation. The scores from these apps showed good predictive ability for both state (suicidal ideation) and trait (future hospitalizations) (Table 4).

A panel of the dozen top biomarkers was combined with measures of anxiety and mood (SASS), and with the suicide risk scale (CFI-S), into a broad spectrum universal predictor (UP Suicide). The UP Suicide provides the biomarkers with mental state (SASS) and personal history context (CFI-S), enhancing precision of predictions (FIGS. 5A-5C and 6 ). Across all subjects in the independent test cohort, UP Suicide 12 had an AUC of 90% (3.87E-21) for state (suicidal ideation) prediction as well as an AUC of 77% (p=2.87E-08) for trait (future hospitalizations for suicidality) predictions. The results for predicting suicidal ideation were even stronger in the low mood subtype (AUC of 92%, p=7.42E-06) and in male bipolars, the highest risk group (AUC 96%, p=8.03E-08). For predicting future hospitalizations, the results were stronger in the high anxiety subtype (AUC 79%, p=7.52E-03), and in male depression (AUC 95%, p=4.88E-04).

Therapeutics

Pharmacogenomics. For phenomenology, the top CFI-S items distinguishing high SI from no SI states were past history of suicidality, social isolation, and dissatisfaction with one's life. The top CFI-S items distinguishing those that had future hospitalizations for suicidality vs. those that did not were past history of suicidality, command auditory hallucinations, and social isolation (FIGS. 4A & 4B). This provides empirical evidence that, in general, reducing social isolation is a good behavioral therapeutic intervention for preventing suicidality. In different individuals different CFI-S items are positive, providing avenues for tailored and targeted (psycho)therapeutic interventions.

A number of individual top biomarkers are targets of medications in current clinical use for treating suicidality, such as lithium (HTR2A, GSK3B, ITGB1BP1, BCL2), clozapine (IL6, CD164, CD47, HTR2A, PGK1, DYRK2, IFNG, LPAR1), and omega-3 fatty acids (APOE, CD47, ACP1, GATM, LHFP, LPAR1) (Tables 4A-4G). In particular, HTR2A and CRYAB are at the overlap of lithium and clozapine, and MBP is at the overlap of all three treatments. Omega-3 fatty acids may be a widely depoyable preventive treatment, with minimal side-effects, including in women who are or may become pregnant.

Bioinformatics drug repurposing analyses using the gene expression biosignature of panels of top biomarkers identified new potential therapeutics for suicidality, such as ebselen (a lithium mimetic), piracetam (a nootropic), chlorogenic acid (a polyphenol from coffee), and metformin (an antidiabetic and possible longevity promoting drug) (Tables 6-18).

Understanding

Biological Pathways. Biological pathway analyses using the Bonferroni validated biomarkers was conducted, which suggested that neurotrophic factors, programmed cell death, and insulin signaling are involved in the biology of suicide (Table 19).

Networks and Interactions. STING analyses revealed groups of directly interactive genes, in particular HTR2A/ARRB1/GSK3B, and SLC4A4/AHCYL1/AHCYL2 (FIG. 8 ), These networks may have biological significance and be targeted therapeutically.

A number of top biomarkers identified have biological roles that are related to the circadian clock (Table 20). To be able to ascertain all the genes in the dataset that were circadian and do estimates for enrichment, from the literature, a database was compiled of all the known genes that fall into these three categories, numbering a total of 1468 genes. Using an estimate of about 21,000 genes in the human genome, that gives about 7% of genes having some circadian pattern. Out of the 154 top biomarker genes, 18 had circadian evidence (11.7%) (Table 20), suggesting a 1.7 fold enrichment for circadian genes. Circadian clock abnormalities are related to mood disorders, and sleep abnormalities have been implicated in suicide.

Enrichment in suicide completers. Of the candidate biomarkers from the Prioritization step, 125/430 of the DE ones (29.1%) and 37/180 of the AP ones (20.6%) were Bonferroni validated in suicide completers. There is a 1.4 fold enrichment in DE vs. AP, which suggests that completion of suicide may be due more to an incremental change in expression of genes rather than the complete turning on and off of genes.

Overall evidence. For the top biomarkers identified, combining all the available evidence from this Example and published literature into a convergent functional evidence (CFE) score (FIG. 7 ), leads to a prioritization of biomarkers for future studies in this field.

TABLE 20 Convergent Functional Evidence (CFE). Universal Top Dozen and Bonferroni biomarkers. Only predictions with a significant p-value for the ROC AUC are tabulated and shown. Step 4 Significant Prediction of Suicidal Step 2 Ideation Convergent All Step 1 Functional Best in Discovery Genomics Step 3 Subtypes in Blood (CFG) Validation Best in (Direction Evidence For in Blood Individualized of Change) Involvement ANOVA Gender/Dx Gene Symbol/ Method/ in Suicide p-value/ ROC AUC/ Gene Name Probesets Score Score Score p-value APOE 203382_s_at (I) 6 3.44E−09/4 All apolipoprotein E DE/1 0.58/2.26E−02 Combined Subtype 0.62/1.99E−02 M-BP 0.71/9.02E−03 IL6 205207_at (I) 6 1.82E−15/4 All interleukin 6 AP/1 0.58/3.74E−02 Combined Subtype 0.61/3.98E−02 CD164 208654_s_at (D) 4 3.01E−08/4 All CD164 DE/2 0.59/1.80E−02 molecule, M-BP sialomucin 0.68/1.94E−02 CD47 211075_s_at (D) 4 1.62E−17/4 All CD47 molecule DE/2 0.6/9.71E−03 Low Mood Subtype 0.68/2.99E−02 M-SZA 0.69/2.19E−02 HTR2A 244130_at (I) 8 NS Low Mood 5- DE/2 Subtype hydroxytryptamine 0.66/4.74E−02 (serotonin) M-SZ receptor 2A, G 0.79/1.58E−02 protein-coupled PGK1 217383_at (D) 4 4.07E−07/4 M-SZA phosphoglycerate DE/2 0.73/8.31E−03 kinase 1 PKP4 201929_s_at (D) 5 3.82E−08/4 Combined plakophilin 4 DE/1 Subtype 0.62/2.59E−02 M-SZ 0.75/2.93E−02 ACP1 1554808_at (D) 6 3.82E−11/4 acid DE/1 phosphatase 1, soluble DYRK2 202969_at (D) 4 1.67E−13/4 All dual-specificity DE/1 0.58/3.37E−02 tyrosine-(Y)- Combined phosphorylation Subtype regulated 0.61/3.00E−02 kinase 2 M-SZ/SZA 0.68/9.85E−03 GATM 1566861_at (I) 4 1.80E−12/4 Combined glycine DE/1 Subtype amidinotransferase 0.6/4.84E−02 (L-arginine: glycine M-BP amidinotransferase) 0.68/1.94E−02 GSK3B 226183_at (D) 6 2.19E−36/4 M-SZA glycogen DE/1 0.68/3.47E−02 synthase kinase 3 beta IFNG 210354_at (D) 8 NS All interferon, AP/1 0.6/1.01E−02 gamma Combined Subtype 0.61/3.03E−02 M-PTSD 0.73/2.72E−02 ITGB1BP1 203337_x_at (D) 4 1.11E−08/4 Low Mood integrin beta 1 DE/1 Subtype binding protein 1 0.67/4.21E−02 M-SZ 0.78/1.64E−02 LHFP 218656_s_at (I) 4 3.97E−10/4 All lipoma HMGIC DE/1 0.57/5.00E−02 fusion partner Anxious Subtype 0.78/1.95E−02 F-BP 0.79/4.60E−02 LPAR1 204036_at (D) 4 1.35E−23/4 M-BP lysophosphatidic AP and DE/1 0.68/2.13E−02 acid receptor 1 PRKCI 209677_at (D) 4 2.71E−05/4 Anxious protein kinase DE/1 Subtype C, iota 0.8/1.55E−02 SKA2 225686_at (D) 8 4.55E−03/2 All spindle and DE/1 0.61/3.35E−03 kinetochore Low Mood associated Subtype complex 0.74/5.91E−03 subunit 2 M-SZ 0.79/1.35E−02 SLC4A4 210739_x_at (I) 6 7.74E−05/4 All solute carrier AP/1 0.64/3.83E−04 family 4 Combined (sodium Subtype bicarbonate 0.69/6.13E−04 cotransporter), M-BP member 4 0.77/9.27E−04 BCL2 203685_at (D) 5 5.98E−11/4 All B-cell DE/1 0.61/4.90E−03 CLL/lymphoma M-SZ 2 0.76/2.73-02 Low Mood Subtype 0.67/4.02E−02 ECHDC1 223087_at (D) 4 3.35E−09/4 All enoyl CoA DE/2 0.6/9.14E−03 hydratase Combined domain Subtype containing 1 0.64/1.04E−02 M-SZA 0.68/3.14E−02 GDI2 200008_s_at (D) 4 1.52E−11/4 All GDP DE/2 0.59/1.26E−02 dissociation M-BP inhibitor 2 0.67/2.39E−02 MTERF4 1557966_x_at (D) 4 6.72E−06/4 All mitochondrial DE/2 0.61/4.64E−03 transcription Low Mood termination Subtype factor 4 0.67/4.21E−02 M-SZ 0.76/2.64E−02 PCDH9 238919_at (D) 4 6.61E−05/4 Combined protocadherin 9 AP/2 Subtype 0.6/4.45E−02 TGOLN2 203834_s_at (D) 5 1.37E−11/4 trans-golgi AP/1 network protein 2 YWHAH 242325_at (I) 4 6.65E−11/4 All tyrosine 3- DE/2 0.57/4.92E−02 monooxygenase/ F-BP tryptophan 5- 0.79/4.60E−02 monooxygenase activation protein, eta ACSM3 210377_at (D) 4 9.67E−06/4 All acyl-CoA DE/1 0.58/2.90E−02 synthetase M-BP medium-chain 0.69/1.35E−02 family member 3 AGA 204333_s_at (D) 4 1.51E−06/4 Combined aspartylglucosaminidase DE/1 Subtype 0.62/2.07E−02 AKAP13 209534_x_at (I) 4 2.06E−05/4 Low Mood A kinase DE/1 Subtype (PRKA) anchor 0.68/3.14E−02 protein 13 M-PTSD 0.78/8.75E−03 AKAP2 202759_s_at (D) 4 5.17E−07/4 Combined A kinase DE/1 Subtype (PRKA) anchor 0.6/4.23E−02 protein 2 ALDH7A1 20895l_at (I) 4 1.58E−07/4 All aldehyde DE/1 0.58/3.55E−02 dehydrogenase M-BP 7 family, 0.68/2.09E−02 member A1 ATP6V0E1 214244_s_at (D) 4 7.84E−07/4 M-SZA ATPase, H+ DE/1 0.76/3.76E−03 transporting, lysosomal 9 kDa, V0 subunit e1 ATP6V0E1 236527_at (D) 4 5.91E−13/4 M-SZA ATPase, H+ AP/1 0.72/1.29E−02 transporting, lysosomal 9 kDa, V0 subunit e1 BRCC3 216521_s_at (D) 4 1.71E−12/4 All BRCA1/BRCA DE/1 0.58/3.74E−02 2-containing M-BP complex, 0.72/6.47E−03 subunit 3 CAT 211922_s_at (D) 4 1.28E−11/4 All catalase DE/1 0.57/3.84E−02 Low Mood Subtype 0.67/4.02E−02 M-BP 0.7/1.14E−02 CTTN 214782_at (I) 4 1.04E−19/4 Combined cortactin DE/1 Subtype 0.61/3.33E−02 M-BP 0.76/1.54E−03 DLG1 202516_s_at (D) 4 1.61E−12/4 All discs, large DE/1 0.58/2.91E−02 homolog 1 Low Mood (Drosophila) Subtype 0.7/2.02E−02 DUSP13 219963_at (I) 4 5.27E−08/4 M-SZA dual specificity AP/1 0.73/9.96E−03 phosphatase 13 ECHDC1 219974_x_at (D) 4 4.00E−14/4 All enoyl CoA DE/1 0.59/1.38E−02 hydratase M-BP domain 0.65/4.48E−02 containing 1 Combined Subtype 0.6/4.34E−02 EFEMP2 209356_x_at (I) 4 2.38E−05/4 Low Mood EGF containing AP/1 Subtype fibulin-like 0.66/4.96E−02 extracellular matrix protein 2 G2E3 223256_at (D) 4 5.19E−09/4 Low Mood G2/M-phase DE/1 Subtype specific E3 0.67/3.56E−02 ubiquitin protein ligase GDI2 200009_at (D) 4 1.47E−05/4 All GDP DE/1 0.64/5.93E−04 dissociation M-BP inhibitor 2 0.74/2.76E−03 Low Mood Subtype 0.69/2.43E−02 IGHG1 211633_x_at (D) 4 6.55E−11/4 M-MDD — AP and 0.79/2.47E−03 DE/1 IL13 207844_at (I) 4 3.38E−08/4 Low Mood interleukin 13 DE/1 Subtype 0.76/3.51E−03 ITGB1BP1 203336_s_at (D) 4 2.54E−08/4 All integrin beta 1 DE/1 0.57/4.15E−02 binding protein 1 ITPKB 232526_at (I) 4 4.46E−09/4 All inositol- AP/1 0.62/1.90E−03 trisphosphate 3- M-BP kinase B 0.76/1.31E−03 Combined Subtype 0.68/1.76E−03 LRRN3 209841_s_at (D) 4 6.69E−10/4 All leucine rich DE/1 0.58/2.37E−02 repeat neuronal 3 M-PTSD 0.77/1.11E−02 MRPS14 203800_s_at (D) 4 3.95E−10/4 M-SZA mitochondrial DE/1 0.72/1.15E−02 ribosomal protein S14 MRPS14 203801_at (D) 4 2.45E−17/4 All mitochondrial DE/1 0.6/6.89E−03 ribosomal M-SZ protein S14 0.72/4.66E−02 Low Mood Subtype 0.69/2.63E−02 N4BP2L2 202259_s_at (D) 4 8.33E−10/4 Low Mood NEDD4 DE/1 Subtype binding protein 0.66/4.63E−02 2-like 2 PIK3CA 231854_at (D) 4 2.41E−37/4 All phosphatidylinositol- DE/1 0.57/4.23E−02 4,5-bisphosphate 3- M-BP kinase, catalytic 0.65/4.64E−02 subunit alpha Non-Affective Subtype 0.74/2.24E−02 PPAP2B 212226_s_at (I) 4 2.76E−17/4 All phosphatidic AP/1 0.58/3.64E−02 acid M-BP phosphatase 0.65/4.56E−02 type 2B Low Mood Subtype 0.75/4.15E−03 PRKAR2B 203680_at (D) 4 3.83E−09/4 F-BP protein kinase, DE/1 0.84/2.69E−02 cAMP- dependent, regulatory, type II, beta PSMB4 202243_s_at (D) 4 6.55E−14/4 All proteasome DE/1 0.6/1.07E−02 (prosome, M-SZA macropain) 0.71/1.67E−02 subunit, beta type, 4 PSME4 237180_at (I) 4 2.64E−36/4 All Proteasome DE/1 0.6/1.11E−02 Activator M-PTSD Subunit 4 0.79/6.82E−03 Low Mood Subtype 0.68/3.47E−02 PTK2 241453_at (I) 4 2.87E−32/4 All protein tyrosine DE/1 0.61/4.53E−03 kinase 2 M-MDD 0.69/3.24E−02 Combined Subtype 0.64/1.04E−02 SECISBP2L 212450_at (D) 4 6.30E−05/4 All SECIS binding DE/1 0.59/2.05E−02 protein 2-like M-BP 0.71/7.49E−03 Low Mood Subtype 0.68/3.47E−02 SEPT8 209000_s_at (I) 4 4.56E−09/4 All septin 8 DE/1 0.58/2.31E−02 M-BP 0.69/1.52E−02 Combined Subtype 0.63/1.53E−02 SNX6 222410_s_at (D) 4 6.82E−06/4 All sorting nexin 6 DE/1 0.62/2.46E−03 M-PTSD 0.69/4.93E−02 Low Mood Subtype 0.72/1.15E−02 SOD2 215078_at (I) 5 2.27E−34/4 superoxide DE/2 dismutase 2, mitochondrial VTA1 223021_x_at (D) 4 3.95E−08/4 All vesicle DE/1 0.57/4.16E−02 (multivesicular M-SZ/SZA body) 0.64/3.26E−02 trafficking 1 Combined Subtype 0.6/4.29E−02 WIPF3 241600_at (D) 4 1.24E−07/4 WAS/WASL DE/1 interacting protein family, member 3 ZNF565 228305_at (D) 4 4.20E−16/4 All zinc finger DE/1 0.59/1.31E−02 protein 565 M-SZA 0.75/4.43E−03 Low Mood Subtype 0.69/2.50E−02 ADK 204119_s_at (D) 0 1.99E−08/4 All adenosine DE/4 0.62/2.58E−03 kinase M-PTSD 0.69/4.93E−02 Combined Subtype 0.64/8.60E−03 AIMP1 227605_at (D) 4 1.02E−05/4 All aminoacyl AP/2 0.6/7.31E−03 tRNA M-SZA synthetase 0.72/1.06E−02 complex- Combined interacting Subtype multifunctional 0.66/3.69E−03 protein 1 AK2 212174_at (D) 2 3.19E−06/4 All adenylate DE/2 0.59/1.71E−02 kinase 2 M-SZ 0.76/2.64-02 Combined Subtype 0.62/2.35E−02 AK2 205996_s_at (D) 2 1.15E−07/4 All adenylate DE/2 0.64/5.39E−04 kinase 2 M-SZ 0.75/2.93E−02 Combined Subtype 0.62/2.04E−02 CD109 226545_at (I) 2 2.16E−09/4 F-BP CD109 DE/2 0.81/3.73E−02 molecule DSPP 221681_s_at (D) 4 7.04E−09/4 All dentin DE/2 0.57/4.26E−02 sialophosphoprotein HIST1H2BO 214540_at (I) 0 5.37E−14/4 M-BP histone cluster DE/4 0.67/2.78E−02 1, H2bo LEPR 211355_x_at (D) 4 4.79E−05/4 leptin receptor DE/2 MAP2K5 216765_at (D) 4 1.74E−08/4 M-SZA mitogen- AP/2 0.67/3.56E−02 activated protein kinase kinase 5 MBP 225408_at (D) 4 8.34E−07/4 myelin basic AP/2 protein MED28 222636_at (D) 4 1.30E−09/4 mediator AP/2 complex subunit 28 PITHD1 229856_s_at (D) 0 6.61E−08/4 F-BP PITH (C- AP/4 0.83/3.00E−02 terminal proteasome- interacting domain of thioredoxin- like) domain containing 1 PRKAR1A 200605_s_at (D) 4 2.47E−06/4 M-BP protein kinase, DE/2 0.72/5.84E−03 cAMP- dependent, regulatory, type I, alpha RBM3 222026_at (D) 4 1.73E−05/4 RNA binding DE/2 motif (RNP1, RRM) protein 3 RIMS3 204730_at (D) 0 6.47E−08/4 regulating AP/4 synaptic membrane exocytosis 3 SCAF11 206989_s_at (D) 4 1.71E−10/4 All SR-related DE/2 0.6/8.62E−03 CTD-associated M-BP factor 11 0.77/8.78E−04 Combined Subtype 0.64/9.60E−03 TBL1XR1 235890_at (D) 2 2.34E−08/4 M-BP transducin AP/2 0.66/3.36E−02 (beta)-like 1 X- Combined linked receptor 1 Subtype 0.62/2.48E−02 ZFYVE21 219929_s_at (D) 4 5.96E−06/4 All zinc finger, AP/2 0.58/2.56E−02 FYVE domain containing 21 ADIRF 203571_s_at (I) 4 6.58E−14/4 M-SZ/SZA adipogenesis DE/1 0.66/2.22E−02 regulatory Low Mood factor Subtype 0.71/1.58E−02 AGA 216064_s_at (D) 4 2.41E−06/4 aspartylglucosaminidase DE/1 AHCYL1 207464_at (D) 4 3.53E−11/4 adenosylhomocysteinase- DE/1 like 1 AKAP10 205045_at (D) 4 4.05E−05/4 All A kinase AP/1 0.58/3.79E−02 (PRKA) anchor M-MDD protein 10 0.76/5.91E−03 ALDH3A2 202053_s_at (D) 4 3.52E−06/4 aldehyde DE/1 dehydrogenase 3 family, member A2 ANKMY1 1554610_at (D) 4 6.19E−15/4 M-PTSD ankyrin repeat DE/1 0.69/4.93E−02 and MYND domain containing 1 ARRB1 218832_x_at (D) 4 5.26E−17/4 arrestin, beta 1 AP/1 B2M 232311_at (I) 4 5.80E−12/4 beta-2- DE/1 microglobulin BCKDHB 213321_at (D) 4 1.72E−11/4 branched chain DE/1 keto acid dehydrogenase E1, beta polypeptide BRCC3 221196_x_at (D) 4 6.11E−12/4 M-BP BRCA1/BRCA DE and 0.73/4.69E−03 2-containing AP/1 Low Mood complex, Subtype subunit 3 0.69/2.50E−02 CAT 201432_at (D) 4 3.39E−14/4 M-BP catalase DE/1 0.69/1.54E−02 Low Mood Subtype 0.7/1.97E−02 CDC42EP4 218062_x_at (D) 4 1.48E−05/4 CDC42 effector AP/1 protein (Rho GTPase binding) 4 CLN5 214252_s_at (D) 4 1.79E−15/4 All ceroid- DE/1 0.65/1.86E−04 lipofuscinosis, M-SZ/SZA neuronal 5 0.68/9.51E−03 Low Mood Subtype 0.75/4.43E−03 CLTA 20405 0_s_at (D) 4 7.07E−11/4 All clathrin, light DE/1 0.6/7.10E−03 chain A M-BP 0.68/2.18E−02 Combined Subtype 0.62/2.48E−02 CLTA 216295_s_at (D) 4 1.74E−15/4 All clathrin, light DE/1 0.64/6.31E−04 chain A M-SZ 0.77/2.20E−02 Combined Subtype 0.67/2.41E−03 DAB2 201279_s_at (I) 4 6.28E−07/4 All Dab, mitogen- DE/1 0.59/1.99E−02 responsive M-PTSD phosphoprotein, 0.72/3.02E−02 homolog 2 (Drosophila) FADS1 208964_s_at (I) 4 3.12E−11/4 M-PTSD fatty acid DE/1 0.7/4.07E−02 desaturase 1 /// microRNA 1908 NGFR 205858_at (I) 4 2.24E−15/4 All nerve growth DE/1 0.59/1.81E−02 factor receptor M-SZA 0.73/9.96E−03 Combined Subtype 0.66/4.27E−03 OLIG1 228170_at (D) 4 9.88E−16/4 oligodendrocyte DE/1 transcription factor 1 PAFAH1B2 210160_at (D) 4 6.61E−18/4 platelet- DE/1 activating factor acetylhydrolase 1b, catalytic subunit 2 □Z̧ POLR2D 214144_at (D) 4 1.38E−13/4 M-SZ/SZA polymerase AP/1 0.63/4.45E−02 (RNA) II (DNA Low Mood directed) Subtype polypeptide D 0.66/4.42E−02 PRKCB 227824_at (D) 4 2.40E−13/4 protein kinase DE and C, beta AP/1 SMCR8 227304_at (D) 4 1.37E−13/4 All Smith-Magenis DE/1 0.58/2.35E−02 syndrome M-SZ chromosome 0.76/2.54E−02 region, Low Mood candidate 8 Subtype 0.69/2.37E−02 SMCR8 227305_s_at (D) 4 5.56E−12/4 M-BP Smith-Magenis DE/1 0.67/2.53E−02 syndrome chromosome region, candidate 8 SMCR8 238434_at (D) 4 2.88E−10/4 Smith-Magenis DE/1 syndrome chromosome region, candidate 8 SPTBN1 200672_x_at (D) 4 4.56E−07/4 spectrin, beta, DE/1 non- erythrocytic 1 TM4SF1 209386_at (I) 4 1.28E−12/4 transmembrane DE/1 4 L six family member 1 TPD52 201691_s_at (D) 4 5.67E−12/4 Low Mood tumor protein DE/1 Subtype D52 0.73/7.59E−03 TTBK1 230191_at (D) 4 4.81E−07/4 tau tubulin DE/1 kinase 1 VAMP3 211749_s_at (D) 4 7.97E−07/4 vesicle- DE/1 associated membrane protein 3 WARS 200628_s_at (D) 4 2.00E−05/4 Anxious tryptophanyl- AP/1 Subtype tRNA 0.73/4.84E−02 synthetase WNK1 202940_at (D) 4 2.38E−12/4 WNK lysine AP/1 deficient protein kinase 1 XRCC5 208643_s_at (D) 4 3.71E−22/4 Combined X-ray repair DE/1 Subtype complementing 0.61/4.03E−02 defective repair in Chinese hamster cells 5 (double-strand- break rejoining) ZNF75D 1553225_s_at (D) 1 5.40E−14/4 All zinc finger AP/4 0.58/2.79E−02 protein 75D M-BP 0.73/4.80E−03 Combined Subtype 0.6/4.61E−02 AIMP1 202542_s_at (D) 4 1.48E−05/4 All aminoacyl DE/4 0.59/1.31E−02 tRNA M-SZA synthetase 0.71/1.45E−02 complex- Low Mood interacting Subtype multifunctional 0.69/2.25E−02 protein 1 FAM63B 214691_x_at (D) 0 6.24E−11/4 family with DE/4 sequence similarity 63, member B FH 203032_s_at (D) 4 8.14E−20/4 fumarate DE/2 hydratase TMEM254 218174_s_at (D) 4 4.56E−15/4 Combined transmembrane DE/2 Subtype protein 254 0.63/1.67E−02 TUBGCP3 215739_s_at (D) 2 3.48E−24/4 M-BP tubulin, gamma DE/2 0.78/7.44E−04 complex Combined associated Subtype protein 3 0.61/3.28E−02 UQCC1 222470_s_at (D) 0 6.99E−33/4 All ubiquinol- DE/4 0.57/4.27E−02 cytochrome c reductase complex assembly factor 1 VIP 206577_at (D) 5 3.76E−14/4 vasoactive DE/1 intestinal peptide AHCYL2 212814_at (D) 4 6.28E−05/4 adenosylhomocysteinase- AP/1 like 2 C20orf27 218081_at (D) 4 3.56E−35/4 chromosome 20 DE/1 open reading frame 27 C8orf74 1569245_at (D) 6 6.63E−08/4 chromosome 8 DE/1 open reading frame 74 DLL1 227938_s_at (D) 4 2.72E−10/4 delta-like 1 DE/1 (Drosophila) FLOT2 211299_s_at (D) 4 1.17E−10/4 flotillin 2 AP/1 MAP2K5 211370_s_at (D) 4 4.24E−05/4 mitogen- DE/1 activated protein kinase kinase 5 MT1E 212859_x_at (I) 4 2.38E−09/4 metallothionein DE/1 1E MTERF4 214364_at (D) 4 3.38E−09/4 mitochondrial AP/1 transcription termination factor 4 NEK9 212299_at (D) 4 1.08E−09/4 M-BP NIMA-related DE/1 0.69/1.75E−02 kinase 9 SRR 222844_s_at (D) 4 1.36E−18/4 serine racemase DE/1 SYNPO2L 219804_at (I) 4 1.12E−09/4 Low Mood synaptopodin DE/1 Subtype 2-like 0.69/2.50E−02 TMEM245 223006_s_at (D) 4 2.10E−08/4 transmembrane DE/1 protein 245 TRAF3 221571_at (D) 4 1.61E−25/4 TNF receptor- DE/1 associated factor 3 TRIM23 210995_s_at (D) 4 3.24E−21/4 tripartite motif DE/1 containing 23 ADAL 239711_at (D) 0 1.23E−05/4 adenosine AP/4 deaminase-like ANKMY1 210486_at (D) 4 6.98E−04/2 M-SZ/SZA ankyrin repeat AP/2 0.67/1.66E−02 and MYND Combined domain Subtype containing 1 0.67/2.08E−03 BF114768 236879_at (I) 0 1.61E−23/4 — DE/4 CDKAL1 214877_at (D) 0 3.66E−14/4 CDK5 DE/4 regulatory subunit associated protein 1-like 1 CENPH 231772_x_at (D) 0 4.47E−15/4 M-SZ centromere DE/4 0.72/4.96E−02 protein H Low Mood Subtype 0.69/2.40E−02 ERG 213541_s_at (D) 0 NS M-SZA V-Ets avian DE/4 0.66/4.96E−02 erythroblastosis Non-Affective virus E26 Subtype oncogene 0.75/1.93E−02 homolog KBTBD2 223585_x_at (D) 2 2.77E−06/4 kelch repeat DE/2 and BTB (POZ) domain containing 2 LDLRAP1 221790_s_at (D) 4 1.97E−32/4 low density DE/4 lipoprotein receptor adaptor protein 1 RPAP3 1557984_s_at (D) 0 1.06E−05/4 RNA AP/4 polymerase II associated protein 3 SET 215780_s_at (D) 0 1.19E−05/4 SET nuclear DE/4 proto-oncogene /// SET pseudogene 4 ///SET-like protein WWP2 1552737_s_at (D) 0 3.71E−06/4 WW domain AP/4 containing E3 ubiquitin protein ligase 2 C14orf180 1558420_at (I) 4 3.21E−10/4 chromosome 14 DE/1 open reading frame 180 LDLRAP1 57082_at (D) 4 1.49E−38/4 low density DE/1 lipoprotein receptor adaptor protein 1 SPATA18 229331_at (I) 4 1.10E−06/4 spermatogenesis DE/1 associated 18 VPREB3 220068_at (D) 4 1.79E−11/4 pre-B DE/1 lymphocyte 3 CCL28 224240_s_at (D) 0 NS chemokine (C- AP/4 C motif) ligand 28 GAB1 242572_at (I) 0 NS F-BP GRB2 AP/4 0.88/1.49E−02 Associated Binding Protein 1 SUMF2 225002_s_at (D) 0 1.69E−08/4 sulfatase DE/4 modifying factor 2 Step 4 Significant Prediction of First Year Hospitalizations for Suicidality All Best in Step 6 Subtypes Step 5 Drugs that Best in Other Modulate the Individualized Psychiatric Biomarker in Gender/Dx and Related Opposite CFE Gene Symbol/ ROC AUC/ Disorders Direction to Polyevidence Gene Name p-value Evidence Suicide Score APOE M-PTSD Aggression Omega-3 19 apolipoprotein E 0.78/4.43E−02 Aging Alcohol Alzheimer's Disease ASD Dementia Depression- related Longevity MDD SZ/SZA PTSD SZ IL6 M-PTSD Aggression Antipsychotics 19 interleukin 6 0.82/2.58E−02 Antipsychotics Antidepressants Anxiety Tocilizumab BP Siltuximab Cognition Dementia Depression Longevity MDD Mood Neurological Panic Personality SZ/SZA PTSD Sleep Stress SZ CD164 M-PTSD BP Clozapine 18 CD164 0.86/1.43E−02 Cocaine molecule, Dependence sialomucin Stress CD47 M-PTSD MDD Clozapine 18 CD47 molecule 0.79/3.72E−02 Stress Omega-3 SZ HTR2A M-SZA Alcohol Clozapine 18 5- 0.72/1.47E−02 Anxiety Lithium hydroxytryptamine BP Valproate (serotonin) MDD Paliperidone, receptor 2A, G SZ Risperidone protein-coupled OCD Loxapine, Response to Quetiapine Antidepressants Olanzapine, Nefazodone Mirtazapine Ziprasidone Aripiprazole PGK1 M-SZA Alcohol Clozapine 18 phosphoglycerate 0.71/1.84E−02 BP Diazepam kinase 1 MDD SZ SZA PKP4 Combined Alcohol Valproate 18 plakophilin 4 Subtype BP 0.68/8.75E−03 MDD SZ/SZA SZ ACP1 acid M-MDD BP Omega-3 17 phosphatase 1, 0.74/3.79E−02 SZ SSRIs soluble Olanzapine DYRK2 M-PTSD Aging Clozapine 17 dual-specificity 0.82/2.58E−02 BP tyrosine-(Y)- MDD phosphorylation Sleep regulated kinase 2 GATM M-PTSD Alzheimer's Omega-3 17 glycine 0.78/4.43E−02 Disease amidinotransferase BP (L-arginine: glycine MDD amidinotransferase) PTSD GSK3B Aging Lithium 17 glycogen Alcohol SSRI synthase kinase BP Antipsychotics 3 beta Dementia Depression Mood Stabilizers Lithium response MDD SZ IFNG M-PTSD SZ Antipsychotics 17 interferon, 0.82/2.58E−02 MDD gamma PTSD Anxiety SZ/SZA ITGB1BP1 Non-Affective Alzheimer's Lithium 17 integrin beta 1 Subtype Disease binding protein 1 0.7/2.59E−02 BP Mood SZ LHFP M-MDD SZ Omega-3 17 lipoma HMGIC 0.98/2.54E−04 fusion partner LPAR1 Anxious Aging Clozapine 17 lysophosphatidic Subtype BP Omega-3 acid receptor 1 0.77/1.33E−02 Longevity Antidepressants MDD Mood PTSD SZ PRKCI Combined BP Ingenol 17 protein kinase Subtype Circadian mebutate C, iota 0.64/2.64E−02 abnormalities Cocaine Dependence MDD SZ SKA2 M-PTSD PTSD 17 spindle and 0.84/1.75E−02 Stress kinetochore associated complex subunit 2 SLC4A4 Circadian Valproate 17 solute carrier abnormalities family 4 Longevity (sodium MDD bicarbonate SZ cotransporter), member 4 BCL2 Aging Lithium 16 B-cell Alcohol Oblimersen CLL/lymphoma 2 Anxiety Paclitaxel BP Mood PTSD SZ ECHDC1 M-PTSD Addictions 16 enoyl CoA 0.84/1.75E−02 BP hydratase PTSD domain containing 1 GDI2 BP Clozapine 16 GDP MDD dissociation Mood inhibitor 2 SZ MTERF4 Non-Affective Stress 16 mitochondrial Subtype transcription 0.67/4.71E−02 termination factor 4 PCDH9 Aging Clozapine 16 protocadherin 9 MDD Omega-3 SZ/SZA SZ TGOLN2 Combined BP Clozapine 16 trans-golgi Subtype Cocaine network protein 2 0.64/3.41E−02 Dependence MDD Stress SZ YWHAH Alcohol Omega-3 16 tyrosine 3- BP Clozapine monooxygenase/ Longevity tryptophan 5- MDD monooxy genase SZ activation protein, eta ACSM3 M-PTSD MDD 15 acyl-CoA 0.79/3.72E−02 Mood synthetase medium-chain family member 3 AGA MDD Haloperidol 15 aspartylglucosaminidase SZ Antidepressants AKAP13 Cocaine Clozapine 15 A kinase Dependence Diazepam (PRKA) anchor Other Haloperidol protein 13 Substances/ Addictions Panic Stress AKAP2 MDD Clozapine 15 A kinase (PRKA) anchor protein 2 ALDH7A1 M-SZA BP 15 aldehyde 0.72/1.47E−02 SZ dehydrogenase Stress 7 family, member A1 ATP6V0E1 Anxious Alcohol 15 ATPase, H+ Subtype BP transporting, 0.76/1.55E−02 MDD lysosomal M-SZA Stress 9 kDa, V0 0.73/1.21E−02 subunit e1 ATP6V0E1 M-SZA Alcohol 15 ATPase, H+ 0.68/3.86E−02 BP transporting, MDD lysosomal Stress 9 kDa, V0 subunit e1 BRCC3 Combined Sleep 15 BRCA1/BRCA Subtype BP 2-containing 0.63/3.85E−02 complex, subunit 3 CAT M-SZA BP 15 catalase 0.70/2.29E−02 Longevity MDD Mood PTSD SZ CTTN BP Clozapine 15 cortactin Effect of Omega-3 valproate Valproate MDD Stress DLG1 Alcohol Omega-3 15 discs, large BP Clozapine homolog 1 MDD (Drosophila) SZ DUSP13 SZ/SZA Olanzapine 15 dual specificity phosphatase 13 ECHDC1 M-PTSD Addictions 15 enoyl CoA 0.79/3.72E−02 BP hydratase PTSD domain containing 1 EFEMP2 Neurological Clozapine 15 EGF containing fibulin-like extracellular matrix protein 2 G2E3 Cocaine Omega-3 15 G2/M-phase Dependence specific E3 ubiquitin protein ligase GDI2 BP Clozapine 15 GDP MDD dissociation Mood inhibitor 2 SZ IGHG1 M-MDD ASD 15 — 0.9/1.64E−03 BP Mood SZ/SZA Stress SZ SZA IL13 MDD CAT-354 15 interleukin 13 SZ ITGB1BP1 Alzheimer's Lithium 15 integrin beta 1 Disease binding protein 1 BP Mood SZ ITPKB Aging Omega-3 15 inositol- Alcohol trisphosphate 3- Alzheimer's kinase B Disease ASD BP MDD Multiple Sclerosis Stress SZ SZA LRRN3 Bipolar disorder Mood 15 leucine rich (Effect of Mood stabilizers repeat neuronal 3 Stibilizers) MRPS14 SZ Omega-3 15 mitochondrial ribosomal protein S14 MRPS14 SZ Omega-3 15 mitochondrial ribosomal protein S14 N4BP2L2 M-PTSD BP 15 NEDD4 0.8/3.11E−02 MDD binding protein SZ 2-like 2 PIK3CA Longevity Lithium 15 phosphatidylinositol- MDD 4,5-bisphosphate 3- Stress kinase, catalytic SZ subunit alpha PPAP2B M-PTSD SZ/SZA 15 phosphatidic 0.83/2.13E−02 SZ acid phosphatase type 2B PRKAR2B Alcohol Clozapine 15 protein kinase, Antipsychotics Valproate cAMP- BP dependent, MDD regulatory, type PTSD II, beta SZ PSMB4 BP Diazepam 15 proteasome MDD (prosome, SZ macropain) SZA subunit, beta type, 4 PSME4 All ASD 15 Proteasome 0.59/2.62E−02 Activator Low Mood Subunit 4 Subtype 0.72/4.73E−02 PTK2 Alcohol CT-707 15 protein tyrosine ASD kinase 2 BP Circadian abnormalities MDD Neurological SZ/SZA Stress SZ SECISBP2L Cocaine Clozapine 15 SECIS binding Dependence protein 2-like MDD SZ SEPT8 M-SZA Alcohol 15 septin 8 0.68/4.14E−02 Epilepsy Mood SZ SNX6 M-PTSD Panic 15 sorting nexin 6 0.83/2.13E−02 SOD2 Longevity Clozapine 15 superoxide MDD Antidepressants dismutase 2, methamphetamine mitochondrial SZ/SZA Mood SZ VTA1 M-SZA BP 15 vesicle 0.67/4.55E−02 MDD (multivesicular SZ body) SZA trafficking 1 WIPF3 M-MDD SZ Clozapine 15 WAS/WASL 0.82/9.58E−03 interacting protein family, member 3 ZNF565 All SZ 15 zinc finger 0.6/2.36E−02 protein 565 M-SZA 0.67/4.81E−02 Anxious Subtype 0.71/3.93E−02 ADK Depression Omega-3 14 adenosine kinase AIMP1 M-PTSD 14 aminoacyl 0.82/2.58E−02 tRNA Non-Affective synthetase Subtype complex- 0.68/3.83E−02 interacting multifunctional protein 1 AK2 All BP 14 adenylate 0.59/3.29E−02 SZ kinase 2 Non-Affective Subtype 0.71/2.05E−02 AK2 All BP 14 adenylate 0.6/2.31E−02 SZ kinase 2 M-SZA 0.78/2.70E−03 Combined Subtype 0.68/6.72E−03 CD109 M-MDD Response to 14 CD109 0.76/2.90E−02 paroxetine molecule (SSRI) DSPP SZ 14 dentin Circadian sialophosphoprotein abnormalities HIST1H2BO Anxious Stress 14 histone cluster Subtype 1, H2bo 0.71/4.20E−02 LEPR Alcohol Antidepressants 14 leptin receptor Cocaine Recombinant- Dependence methionyl human MDD leptin Mood Other Substances/ Addictions MAP2K5 Agoraphobia 14 mitogen- BP activated MDD protein kinase Methamphetamine kinase 5 dependence Other Substances/ Addictions MBP Alcohol Clozapine 14 myelin basic Alzheimer's Omega-3 protein Disease Lithium BP MDD Mood Neurological SZ MED28 M-PTSD Alcohol 14 mediator 0.83/2.13E−02 BP complex PTSD subunit 28 PITHD1 M-PTSD BP 14 PITH (C- 0.78/4.43E−02 SZ/SZA terminal SZ proteasome- interacting domain of thioredoxin- like) domain containing 1 PRKAR1A Alcohol 14 protein kinase, BP cAMP- Epilepsy dependent, Mood regulatory, type Stress I, alpha SZ RBM3 Epilepsy Omega-3 14 RNA binding Response to Valproate motif (RNP1, Lithium RRM) protein 3 (Bipolar) SZ RIMS3 Non-Affective Alcohol Clozapine 14 regulating Subtype Antipsychotics Haloperidol synaptic 0.73/1.37E−02 BP membrane SZ exocytosis 3 SCAF11 BP 14 SR-related Mood CTD-associated factor 11 TBL1XR1 Alcohol Clozapine 14 transducin BP (beta)-like 1 X- Longevity linked receptor 1 ZFYVE21 SZ 14 zinc finger, FYVE domain containing 21 ADIRF BP 13 adipogenesis regulatory factor AGA MDD Haloperidol 13 aspartyl glucosaminidase SZ Antidepressants AHCYL1 SZ Omega-3 13 adenosylhomocysteinase- like 1 AKAP10 BP 13 A kinase (PRKA) anchor protein 10 ALDH3A2 M-PTSD BP 13 aldehyde 0.83/2.13E−02 dehydrogenase Combined 3 family, Subtype member A2 0.63/4.65E−02 ANKMY1 M-MDD 13 ankyrin repeat 0.76/2.71E−02 and MYND domain containing 1 ARRB1 M-SZA Alcohol 13 arrestin, beta 1 0.69/3.35E−02 MDD Combined Personality Subtype Response to 0.65/2.19E−02 paroxetine (SSRI) Stress B2M Alcohol Omega-3 13 beta-2- Effect of microglobulin valproate MDD SZ BCKDHB All MDD 13 branched chain 0.59/3.90E−02 SZ/SZA keto acid M-SZ dehydrogenase 0.67/3.74E−02 E1, beta Non-Affective polypeptide Subtype 0.7/2.53E−02 BRCC3 Sleep 13 BRCA1/BRCA BP 2-containing complex, subunit 3 CAT BP 13 catalase Longevity MDD Mood PTSD SZ CDC42EP4 All Aging 13 CDC42 effector 0.59/2.91E−02 Alcohol protein (Rho M-MDD MDD GTPase 0.85/5.84E−03 binding) 4 Low Mood Subtype 0.84/5.28E−03 CLN5 M-PTSD 13 ceroid- 0.87/1.16E−02 lipofuscinosis, neuronal 5 CLTA Alzheimer's 13 clathrin, light Disease chain A BP MDD CLTA Alzheimer's 13 clathrin, light Disease chain A BP MDD DAB2 SZ/SZA 13 Dab, mitogen- responsive phosphoprotein, homolog 2 (Drosophila) FADS1 Aging 13 fatty acid Antipsychotics desaturase 1 SZ /// microRNA 1908 NGFR MDD 13 nerve growth OCD factor receptor Panic Disorder SZ OLIG1 Non-Affective Agreeableness 13 oligodendrocyte Subtype SZ transcription 0.69/3.08E−02 factor 1 PAFAH1B2 Lithium effect Lithium 13 platelet- activating factor acetylhydrolase 1b, catalytic subunit 2 □Z̧ POLR2D BP 13 polymerase (RNA) II (DNA directed) polypeptide D PRKCB Aging Lithium 13 protein kinase ASD Ingenol mebutate C, beta BP MDD PTSD Stress SZ SMCR8 MDD 13 Smith-Magenis Anxiety syndrome chromosome region, candidate 8 SMCR8 MDD 13 Smith-Magenis Anxiety syndrome chromosome region, candidate 8 SMCR8 Combined MDD 13 Smith-Magenis Subtype Anxiety syndrome 0.63/4.42E−02 chromosome region, candidate 8 SPTBN1 Aging Clozapine 13 spectrin, beta, BP Omega-3 non- Longevity Diazepam erythrocytic 1 MDD SZ TM4SF1 SZ Lithium 13 transmembrane BP Omega- 4 L six family Antipschotic member 1 TPD52 BP 13 tumor protein Mood D52 Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome SZ TTBK1 SZ Clozapine 13 tau tubulin kinase 1 VAMP3 Alcohol Lithium 13 vesicle- lithium effect associated MDD membrane Stress protein 3 valproate effect WARS Alcohol 13 tryptophanyl- SZ tRNA synthetase WNK1 Alcohol Omega-3 13 WNK lysine BP SSRI deficient Cocaine protein kinase 1 Dependence MDD SZ XRCC5 Alcohol 13 X-ray repair BP complementing Longevity defective repair MDD in Chinese hamster cells 5 (double-strand- break rejoining) ZNF75D Circadian 13 zinc finger abnormalities protein 75D Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome AIMP1 12 aminoacyl tRNA synthetase complex- interacting multifunctional protein 1 FAM63B BP Clozapine 12 family with Mood sequence Sleep similarity 63, SZ member B FH BP 12 fumarate MDD hydratase Stress TMEM254 12 transmembrane protein 254 TUBGCP3 BP 12 tubulin, gamma complex associated protein 3 UQCC1 BP 12 ubiquinol- cytochrome c reductase complex assembly factor 1 VIP Alcohol 12 vasoactive BP intestinal MDD peptide SZ AHCYL2 ASD 11 adenosylhomocysteinase- like 2 C20orf27 BP 11 chromosome 20 MDD open reading frame 27 C8orf74 11 chromosome 8 open reading frame 74 DLL1 BP 11 delta-like 1 PTSD (Drosophila) SZ FLOT2 SZ 11 flotillin 2 MAP2K5 Agoraphobia 11 mitogen- BP activated MDD protein kinase Methamphetamine kinase 5 dependence Other Substances/ Addictions MT1E BP 11 metallothionein SZ 1E SZ/SZA MTERF4 Stress 11 mitochondrial transcription termination factor 4 NEK9 11 NIMA-related kinase 9 SRR SZ 11 serine racemase SYNPO2L 11 synaptopodin 2-like TMEM245 BP 11 transmembrane MDD protein 245 Stress TRAF3 BP 11 TNF receptor- MDD associated Neurological factor 3 Stress SZ SZA TRIM23 BP 11 tripartite motif SZ containing 23 ADAL Mood 10 adenosine Circardian deaminase-like abnormalities ANKMY1 10 ankyrin repeat and MYND domain containing 1 BF114768 Non-Affective 10 — Subtype 0.69/3.36E−02 CDKAL1 Alcohol 10 CDK5 BP regulatory SZ subunit associated protein 1-like 1 CENPH 10 centromere protein H ERG Low Mood Alcohol 10 V-Ets avian Subtype erythroblastosis 0.82/8.29E−03 virus E26 oncogene homolog KBTBD2 M-SZA 10 kelch repeat 0.7/2.43E−02 and BTB (POZ) domain containing 2 LDLRAP1 10 low density lipoprotein receptor adaptor protein 1 RPAP3 SZ/SZA 10 RNA polymerase II associated protein 3 SET Alzheimer's 10 SET nuclear Epilepsy proto-oncogene /// SET pseudogene 4 ///SET-like protein WWP2 Alcohol 10 WW domain SZ containing E3 ubiquitin protein ligase 2 C14orf180 9 chromosome 14 open reading frame 180 LDLRAP1 9 low density lipoprotein receptor adaptor protein 1 SPATA18 9 spermatogenesis associated 18 VPREB3 9 pre-B lymphocyte 3 CCL28 Circardian SSRI 8 chemokine (C- abnormalities C motif) ligand Mood 28 GAB1 Alcohol 8 GRB2 BP Associated Delusions Binding Protein Hallucinations 1 SUMF2 8 sulfatase modifying factor 2

Biological pathway analyses were conducted using the top biomarkers, which suggest that neurotrophic factors, programmed cell death, and insulin signaling are involved in the biology of suicide (Table 19).

For the top biomarkers identified, combining all the available evidence from this current Example and the published literature, into a convergent functional evidence (CFE) score (FIG. 7 ), leads to a prioritization of biomarkers for future studies in the field.

Example 2

As a comparator to the universal approach across gender and diagnoses, in this Example, a within-participant longitudinal biomarker discovery analyses in male bipolars only, the largest subgroup (n=20 participants, 65 testing visits) in our discovery cohort, was conducted.

Male bipolars are the highest risk group for suicide clinically, and have been the focus of earlier suicide biomarker studies, with an N that was less than half of the current one (n=9). The discovery step was followed by prioritization, and by validation in male suicide completers. Some of the previous biomarker findings in bipolar disorder (Tables 3B and FIGS. 3C & 3D) were reproduced and examined in this Example. The top dozen biomarkers (Table 3B), and all the biomarkers that survived Bonferroni correction after the validation, for prediction of suicidal ideation and for prediction of future psychiatric hospitalizations due to suicidality in the male bipolar subgroup (n=49) in the independent test cohort (FIGS. 3C & 3D & 9 ).

TABLE 21 Universal Biomarkers - Predictions In Male Bipolars A. Predicting Suicidal Ideation State In Independent Sub-Cohort Of Male Bipolars Participants Suicidality Severity with high SI/ (HAMD SI Score) Participants ROC AUC/ Correlation R/ T-test Markers Cohort total p-value p-value p-value Male Bipolar Best Biomarkers SLC4A4 M-BP 12/130 0.77/9.27E−04 0.24/3.20E−03 1.06E−03 TUBGCP3 M-BP 12/130 0.78/7.44E−04 −0.21/7.99E−03  1.46E−04 BioM 148 Panel M-BP 12/130  0.7/1.27E−02 0.17/2.81E−02 4.06E−03 (Bonferroni List) BIOM 12 M-BP 12/130 0.73/4.07E−03 0.19/1.72E−02 5.48E−03 (Top Dozen List) BioM 2 M-BP 12/130 0.80/2.97E−04 0.26/1.63E−03 8.59E−05 (SLC4A4 and TUBGCP3) Phenes Mood M-BP 12/130  0.8/3.65E−04 −0.47/6.83E−09  1.65E−03 Anxiety M-BP 12/130 0.86/2.19E−05 0.41/7.09E−07 1.91E−05 Mood and Anxiety M-BP 12/130 0.86/1.66E−05  0.5/7.15E−10 5.66E−05 CFI-S M-BP 12/128 0.92/1.10E−06  0.5/6.11E−10 1.31E−06 Mood and Anxiety and M-BP 12/128 0.94/2.82E−07 0.61/1.24E−14 3.01E−06 CFI-S Phenes and Biomarkers Mood and Anxiety and M-BP 12/128 0.95/1.55E−07 0.62/1.71E−15 1.21E−06 CFI-S and BioM 148 Mood and Anxiety and M-BP 12/128 0.96/8.03E−08 0.63/6.05E−16 4.79E−07 CFI-S and BioM 12 Mood and Anxiety and M-BP 12/128 0.96/9.58E−08 0.62/2.20E−15 3.91E−07 CFI-S and BioM 2 B. Prediction Of Future Hospitalizations For Suicidality Within First Year Of Testing Visit In Independent Sub-Cohort Of Male Bipolars Participants Frequency with of future future hospitalizations hospitalizations for suicidality Cox for suicidality within the Regression within the first first year Hazard year/Particpants ROC AUC/ Correlation R/ T-test Ratio/ Biomarker Cohort total p-value p-value p-value P-value Male Bipolar Best Biomarkers PPAP2B M-BP 4/120 0.74/5.08E−02 0.11/1.15E−01 7.74E−02 1.52/2.28E−01 ALDH3A2 M-BP 4/120 0.77/3.38E−02 −0.15/5.25E−02  4.15E−02 2.43/1.02E−01 BioM 148 Panel M-BP 4/120 0.52/4.48E−01 0.01/4.56E−01 4.66E−01 1.13/9.18E−01 (Bonferroni List) BIOM 12 M-BP 4/120 0.67/1.21E−01 0.08/1.95E−01 1.85E−01 2.65/3.76E−01 (Top Dozen List) BioM 2 M-BP 4/120 0.77/2.97E−02 0.15/5.50E−02 5.59E−02 6.29/6.95E−02 (PPAP2B and ALDH3A2) Phenes Mood M-BP 4/120 0.69/1.04E−01 −0.14/6.08E−02  2.75E−01 2.10/1.32E−01 Anxiety M-BP 4/120  0.7/9.29E−02 0.12/9.74E−02 1.12E−01 1.87/2.09E−01 Mood and Anxiety M-BP 4/120 0.72/7.19E−02 0.15/5.27E−02 1.34E−01 1.52/1.18E−01 CFIS M-BP 4/120 0.80/2.10E−02 0.15/5.22E−02 3.46E−03 1.95/1.21E−01 Mood and Anxiety and M-BP 4/120 0.78/2.77E−02 0.18/2.36E−02 6.78E−02 1.41/5.54E−02 CFIS Phenes and Biomarkers Mood and Anxiety and M-BP 4/120 0.77/3.49E−02 0.18/2.56E−02 8.84E−02 1.38/6.06E−02 CFI-S and BioM 148 Mood and Anxiety and M-BP 4/120 0.79/2.51E−02 0.19/1.75E−02 6.30E−02 1.42/4.35E−02 CFI-S and BioM 12 Mood and Anxiety and M-BP 4/120 0.84/1.13E−02 0.22/7.95E−03 3.67E−02 0.96/8.38E−01 CFI-S and BioM 2 C. Prediction Of All Future Hospitalizations For Suicidality Following Testing In Independent Sub-Cohort Of Male Bipolars Participants Frequency with future of future hospitalizations hospitalizations for suicidality/ for suicidality Cox Participants Correlation R/ Regression/ Predictors Cohort total p-value P-value Best Biomarkers Male Bipolar Best Biomarkers TM4SF1 Male Bipolar 9/121 0.11/1.07E−01 1.41/2.78E−01 ADAL Male Bipolar 9/121 −0.17/3.14E−02  1.42/3.98E−01 BioM 148 Panel Male Bipolar 9/121 −0.04/6.74E−01  1.15/8.61E−01 (Bonferroni List) BIOM 12 Male Bipolar 9/121 0.04/3.43E−01 7.97/2.44E−01 (Top Dozen List) BioM 2 Male Bipolar 9/121 0.18/2.21E−02 1.32/5.25E−01 (TM4SF1 and ADAL) Phenes Mood Male Bipolar 9/121 −0.07/2.30E−01  1.86/6.72E−02 Anxiety Male Bipolar 9/121 0.31/3.27E−04 4.00/1.10E−03 Mood and Anxiety Male Bipolar 9/121 0.21/9.74E−03 1.77/2.71E−03 CFI-S Male Bipolar 9/121 0.25/2.91E−03 2.78/7.90E−04 Mood and Anxiety and Male Bipolar 9/121 0.27/1.17E−03  1.6/1.11E−04 CFI-S Phenes and Biomarkers Mood and Anxiety and Male Bipolar 9/121 0.26/2.04E−03 1.55/1.47E−04 CFI-S and BioM 148 Mood and Anxiety and Male Bipolar 9/121 0.28/1.07E−03 0.96/7.12E−01 CFI-S and BioM 12 Mood and Anxiety and Male Bipolar 9/121 0.32/1.55E−04 0.98/8.10E−01 CFI-S and BioM 2 Bold - p-value of Correlation survives correction for multiple testing. Correlation is our apriori primary measure. HAMD SI is the suicide rating question from the Hamilton Rating Scale for Depression. * Smaller cohort, as not everybody had HAMD SI information.

This Example was successful in the identification of predictive biomarkers that might be more specific for suicidality in male bipolars. Also examined was whether biomarkers discovered using just male bipolar subjects yielded even better predictors for male bipolar subjects than using the universal biomarkers. It was found that to be the case for trait (hospitalizations) predictions (FIG. 3D). For the top male bipolar biomarkers identified, a number of individual top biomarkers are targets of medications in current clinical use for treating suicidality. Bioinformatics drug repurposing analyses using the gene expression biosignature of panels of top biomarkers identified new potential therapeutics for suicidality in male bipolars. The top compounds identified include betulin (a natural plant compound with anticancer properties), carteolol (a non-specific beta-blocker used for glaucoma), alpha-ergocryptine (an ergot alkaloid and nonspecific serotonin agonist used for migraines), and baclofen (a derivative of GABA used as a muscle relaxant). Combining all the available evidence from this Example and the published literature, into a convergent functional evidence (CFE) score, leads to a prioritization of biomarkers for future studies in the field.

TABLE 22 Convergent Functional Evidence (CFE). Male bipolar Top Dozen and Bonferroni biomarkers. Only predictions with a significant p-value for the ROC AUC are shown. Those that do not have a significant p-value are marked NA. Step 1 Step 4 Step 4 Discovery Step 2 Step 3 Significant Significant in Convergent Validation Prediction of Prediction of Blood Evidence in Suicidal First Year (Direction For Blood Ideation in Hospitalizations of Involvement ANOVA Male Bipolars for Suicidality Gene Symbol/ Change)/ in p-value/ ROC AUC/ in Male Bipolars Gene Name Probesets Score Suicide Score p-value ROC AUC/p-value HTR2A 244130_at (I) 8.00 NS 0.65/ NA 5- DE/2 4.45E−02 Hydroxytryptamine Receptor 2A SAT1 213988_s_at (I) 6.00 4.06E−34/4 NA NA spermidine/ DE/2 spermine N1- acetyltransferase 1 SAT1 210592_s_at (I) 6.00 4.00E−33/4 NA NA spermidine/ DE/2 spermine N1- acetyltransferase 1 CRYAB 209283_at (I) 4.00 3.49E−05 0.65/ NA crystalline, DE/1 4.41E−02 alpha B PIK3R1 239476_at (I) 4.00 2.97E−12 NA 0.81/ Phosphoinositide- DE/1 1.64E−02 3-Kinase Regulatory Subunit 1 PTK2 241453_at (I) 4.00 4.29E−16/4 0.66/ NA Protein DE/2 3.64E−02 Tyrosine Kinase 2 SAT1 203455_s_at (I) 6.00 9.99E−29/4 NA NA spermidine/ DE/1 spermine N1- acetyltransferase 1 SPTBN1 215918_s_at (I) 4.00  6.7E−32/4 0.72/ NA spectrin, AP/1 6.62E−03 beta, non- erythrocytic 1 AKT1S1 1555821_a_at (D) 4.00 8.69E−09/4 NA NA AKT1 DE/2 substrate 1 (proline-rich) AKT1S1 224982_at (D) 4.00 8.04E−11/4 NA NA AKT1 AP/1 and substrate 1 DE/2 (proline-rich) ARHGAP26 205068_s_at (I) 5.00 7.99E−08/4 NA NA Rho GTPase DE/1 activating protein 26 B2M 232311_at (I) 4.00 5.43E−06/4 NA NA beta-2- DE/2 microglobulin PSME4 237180_at (I) 4.00 2.02E−16/4 0.69/ NA Proteasome DE/2 1.41E−02 Activator Subunit 4 ACSM3 210377_at (D) 4.00 2.31E−10/4 0.69/ NA acyl-CoA DE/1 1.35E−02 synthetase medium-chain family member 3 ADORA1 205481_at (D) 4.00 1.19E−07/4 NA NA adenosine A1 DE/1 receptor FAAH 204231_s_at (D) 4.00 7.47E−12/4 NA NA fatty acid DE/1 amide hydrolase MARCKS 213002_at (I) 4.00 7.35E−08/4 NA NA Myristoylated DE/1 alanine-rich protein kinase C substrate MBP 225408_at (D) 4.00 3.26E−06/4 NA NA myelin basic AP/1 protein PAFAH1B2 210160_at (D) 4.00 4.85E−09/4 NA NA platelet-activating DE/1 factor acetylhydrolase 1b, catalytic subunit 2 (30 kDa) PCDH9 238919_at (D) 4.00 4.52E−05/4 NA NA Protocadherin 9 AP/1 PIK3R1 212240_s_at (I) 4.00 7.11E−14/4 NA NA phosphoinositide- DE/1 3-kinase, regulatory subunit 1 (alpha) PTEN 222176_at (I) 4.00 4.88E−05/4 NA 0.9/ phosphatase DE/1 3.27E−03 and tensin homolog RNF6 ring finger 210932_s_at (D) 4.00 1.25E−05/4 NA 0.82/ protein DE/1 1.58E−02 (C3H2C3 type) 6 SLC5A3 solute 213167_s_at (D) 4.00 4.89E−14/4 NA NA carrier family 5 DE/1 (sodium/myoinositol cotransporter), member 3 C20orf27 218081_at (D) 4.00 1.09E−34/4 NA NA chromosome 20 DE/2 open reading frame 27 C7orf73 224758_at (D) 4.00 4.72E−06/4 0.75/ NA Chromosome 7 DE/2 2.38E−03 open reading frame 73 CLYBL 239683_at (D) 4.00    0.009/2 NA NA Citrate AP/4 Lyase Beta Like EZR 208623_s_at (I) 5.00 3.92E−11/4 NA NA ezrin DE/1 ICAM4 207194_s_at (D) 0.00 3.81E−08/4 0.67/ NA intercellular adhesion DE/4 2.83E−02 molecule 4 (Landsteiner-Wiener blood group) NEAT1 224565_at (I) 4.00 9.99E−20/4 NA NA nuclear paraspeckle DE/2 assembly transcript 1 (non-protein coding) NUB1 234332_at (I) 0.00 8.11E−10/4 NA 0.75/ Negative regulator of DE/4 4.78E−02 ubiquitin-like proteins 1 PGBD2 238004_at (D) 0.00 1.25E−05/4 0.72/ NA PiggyBac Transposable AP/4 6.77E−03 Element Derived 2 C8orf74 1569245_at (D) 6.00 3.82E−08/4 NA NA chromosome 8 DE/1 open reading frame 74 CALR 212953_x_at (I) 4.00 1.12E−10/4 NA NA calreticulin DE/1 CRHR1 214619_at (D) 6.00 NS NA NA Corticotropin- DE/1 Releasing Hormone Receptor 1 DLL1 227938_s_at (D) 4.00 1.17E−09/4 NA NA delta-like 1 DE/1 (Drosophila) FADS1 208963_x_at (I) 4.00 1.58E−05/4 NA NA fatty acid AP/1 desaturase 1 KLK7 239381_at (D) 4.00 2.79E−05/4 NA NA Kallikrein Related AP/1 Peptidase 7 MED28 222635_s_at (D) 4.00 1.63E−15/4 NA NA mediator complex DE/1 subunit 28 NDUFS1 239268_at (D) 4.00 3.72E−11/4 NA NA NADH:Ubiquinone DE/1 Oxidoreductase Core Subunit S1 POLR2D 214144_at (D) 4.00  2.1E−08/4 NA NA polymerase (RNA) II AP/1 (DNA directed) polypeptide D PPAP2B 212230_at (I) 4.00 2.49E−06/4 NA NA phosphatidic acid DE/1 phosphatase type 2B SELENBP1 214433_s_at (D) 4.00 7.24E−05/4 NA NA selenium DE/1 binding protein 1 TRIM23 210995_s_at (D) 4.00 3.98E−19/4 NA NA tripartite motif DE/1 containing 23 WARS 200628_s_at (D) 4.00  3.8E−06/4 NA NA tryptophanyl- AP/1 tRNA synthetase ADAL 239711_at (D) 0.00 4.53E−08/4 NA NA Adenosine AP/4 Deaminase-Like ATP13A2 218608_at (D) 4.00 4.75E−08/4 NA NA ATPase DE/2 type 13A2 CNOT3 211141_s_at (D) 0.00 4.05E−16/4 NA NA CCR4-NOT DE/4 transcription complex, subunit 3 JMJD1C 228793_at (I) 0.00  3.6E−06/4 NA NA jumonji domain DE/4 containing 1C KSR1 213769_at (I) 4.00 NS NA NA kinase suppressor AP/4 of ras 1 RPAP3 1557984_s_at (D) 0.00 4.34E−06/4 NA NA RNA polymerase II AP/4 associated protein 3 SORBS1 211705_s_at (D) 2.00 8.95E−11/4 NA NA sorbin and DE/2 SH3 domain containing 1 TDG 203742_s_at (I) 2.00 1.04E−16/4 NA NA thymine-DNA DE/2 glycosylase ZNF302 218490_s_at (D) 0.00 3.87E−05/4 NA NA zinc finger AP/4 protein 302 AIMP1 202542_s_at (D) 4.00 1.73E−05/4 NA NA aminoacyl tRNA DE/1 synthetase complex- interacting multifunctional protein 1 FIGNL1 222843_at (D) 4.00 2.08E−05/4 NA NA fidgetin-like 1 AP/1 MRTO4 235783_at (D) 4.00 7.52E−16/4 NA NA mRNA turnover 4 DE/1 homolog (S. cerevisiae) BF114768 236879_at (I) 0.00 2.62E−12/4 NA NA DE/4 BE674182 237259_at (I) 0.00 NS 0.66/ NA DE/4 3.33E−02 CACNA1I 208299_at (I) 0.00 NS NA NA calcium channel, AP/4 voltage- dependent, T type, alpha 1I subunit Step 5 Step 6 Other Psychiatric and Drugs that Modulate the CFE Gene Symbol/ Related Disorders Biomarker in Opposite Polyevidence Gene Name Evidence Direction to Suicide Score HTR2A Alcohol Clozapine 16 5- Anxiety Lithium Hydroxytryptamine BP Valproate Receptor 2A MDD Paliperidone, Risperidone, SZ lurasidone, clozapine, OCD doxepin, desipramine, , Response to clomipramine, loxapine, Antidepressants quetiapine, olanzapine, nefazodone, mirtazapine, amitriptyline lisuride, sertindole, ziprasidone, mesoridazine, thioridazine, aripiprazole, methysergide, dihydroergotamine, apomorphine, ergotamine, azatadine SAT1 MDD Omega 3 16 spermidine/ Anxiety spermine N1- Mood Disorders acetyltransferase 1 NOS SAT1 MDD Omega 3 16 spermidine/ Anxiety spermine N1- Mood Disorders acetyltransferase 1 NOS CRYAB Autism Lithium 15 crystalline, Alcohol Clozapine alpha B PTSD Methamphetamine SZA BP SZ Insomnia Social Isolation Stress MDD PIK3R1 Schizophrenia Mood 15 Phosphoinositide- MDD Stabilizers 3-Kinase Relaxation Response Regulatory PTSD Subunit 1 BP Longevity Stress Alcohol Insomnia Anxiety PTK2 Alcohol CT-707 15 Protein ASD Tyrosine BP Kinase 2 Circadian abnormalities MDD Neurological SZ/SZA Stress SZ SAT1 MDD Omega 3 15 spermidine/ Anxiety spermine N1- Mood Disorders acetyltransferase 1 NOS SPTBN1 Aging Clozapine 15 spectrin, BP Omega-3 beta, Longevity Diazepam non- MDD erythrocytic 1 SZ AKT1S1 Circadian Omega-3 14 AKT1 abnormalities fatty acids substrate 1 Aging (proline-rich) AKT1S1 Circadian (I) 14 AKT1 abnormalities Brain substrate 1 Longevity Omega-3 (proline-rich) fatty acids¹⁹⁵ ARHGAP26 BP Clozapine 14 Rho GTPase MDD activating Panic Disorder protein 26 SZ B2M Alcohol Omega-3 14 beta-2- Effect of valproate microglobulin MDD SZ PSME4 ASD 14 Proteasome MDD Activator Subunit 4 ACSM3 MDD 13 acyl-CoA Mood synthetase medium-chain family member 3 ADORA1 Alcohol (I) Ventral 13 adenosine A1 SZ tegmentum receptor BP Clozapine¹⁹⁴ Mood, Stimulants Depression FAAH Alcohol (D)FAAH 13 fatty acid SZ Hippocampus amide BP (males) hydrolase MDD Omega-3¹⁹³ Pain Placebo PTSD Stress Hallucinogens Social Isolation MARCKS BP (D) 13 Myristoylated SZ Cerebral alanine-rich MDD Cortex protein Yohimbine (right) kinase Alcohol Lithium¹⁹⁹ C substrate Panic Disorder MBP Alcohol Clozapine 13 myelin basic Alzheimer's Disease Omega-3 protein BP Lithium MDD Mood Neurological SZ PAFAH1B2 MDD Lithium 13 platelet-activating PCP factor Clozapine acetylhydrolase 1b, catalytic subunit 2 (30 kDa) PCDH9 Aging Clozapine 13 Protocadherin 9 MDD Omega-3 SZ/SZA SZ PIK3R1 SZ (D) 13 phosphoinositide- MDD Amygdala 3-kinase, Relaxation Response mood regulatory PTSD stabilizers¹⁹⁸ subunit 1 BP (alpha) Longevity Hallucinogens Stress Alcohol Insomnia Anxiety PTEN SZ 13 phosphatase MDD and BP tensin PTSD homolog Longevity Hallucinogens Stress Yohimbine Alcohol Stimulants Anxiety RNF6 ring finger BP 13 protein Social (C3H2C3 type) 6 Isolation SLC5A3 solute Chronic Stress frontal 13 carrier family 5 MDD cortex (sodium/myoinositol Alcohol Lithium¹⁹⁷ cotransporter), member 3 C20orf27 BP 12 chromosome 20 MDD open reading frame 27 C7orf73 12 Chromosome 7 open reading frame 73 CLYBL MDD 12 Citrate Delusions Lyase Stimulants Beta Like ADHD Longevity Alcohol EZR SZ 12 ezrin Mood Disorders NOS Stimulants Anxiety Alcohol ICAM4 MDD 12 intercellular adhesion molecule 4 (Landsteiner-Wiener blood group) NEAT1 Clozapine 12 nuclear paraspeckle assembly transcript 1 (non-protein coding) NUB1 (D) 12 Negative regulator of NUB1 ubiquitin-like Ventral tegmentum proteins 1 Clozapine¹⁹⁴ PGBD2 BP 12 PiggyBac Transposable Mood State Element Derived 2 C8orf74 11 chromosome 8 open reading frame 74 CALR SZ 11 calreticulin MDD Relaxation Response Pain Longevity Stimulants SZA Alcohol Chronic Stress CRHR1 SZ “Ventral tegmentum 11 Corticotropin- MDD (D) Releasing Pain (Treatments, Hormone Panic Disorder Cognition, Receptor 1 ASD Antipsychotics)¹⁹⁴ Depression Amygdala Alcohol (D) Substances/Addictions (Addictions, SSRI Alcohol, PTSD Alcohol)²⁰¹ Anxiolytics Amygdala BP (paradigm 3) Aggression (I) SNRI (Addictions, Longevity Alcohol, Stress Alcohol)²⁰² Alcohol Antipsychotics Anxiety DLL1 BP 11 delta-like 1 PTSD (Drosophila) SZ FADS1 Aging 11 fatty acid Antipsychotics desaturase 1 SZ KLK7 BP 11 Kallikrein Related Mood Peptidase 7 State MED28 Alcohol 11 mediator complex BP subunit 28 PTSD NDUFS1 Alcohol 11 NADH: Ubiquinone SZ Oxidoreductase Circadian Core Subunit S1 abnormalities POLR2D BP 11 polymerase (RNA) II (DNA directed) polypeptide D PPAP2B SZ/SZA 11 phosphatidic acid SZ phosphatase type 2B SELENBP1 SZ 11 selenium Psychosis binding Circadian abnormalities protein 1 ASD TRIM23 BP 11 tripartite motif SZ containing 23 WARS Alcohol 11 tryptophanyl- SZ tRNA synthetase ADAL Circadian 10 Adenosine abnormalities Deaminase-Like Mood ATP13A2 10 ATPase type 13A2 CNOT3 BP 10 CCR4-NOT Hallucinogens transcription complex, subunit 3 JMJD1C BP PTSD 10 jumonji domain Anxiety containing 1C Hallucinogens KSR1 Hallucinogens 10 kinase suppressor MDD of ras 1 RPAP3 SZ/SZA 10 RNA polymerase II associated protein 3 SORBS1 ASD 10 sorbin and SZ SH3 Longevity domain Mood Disorders containing 1 NOS MDD BP TDG Alcohol 10 thymine-DNA Chronic glycosylase Stress ZNF302 MDD 10 zinc finger SZ protein 302 Post-Traumatic Stress Disorder AIMP1 9 aminoacyl tRNA synthetase complex- interacting multifunctional protein 1 FIGNL1 9 fidgetin-like 1 MRTO4 9 mRNA turnover 4 homolog (S. cerevisiae) BF114768 8 BE674182 6 CACNA1I MDD 6 calcium channel, SZ voltage- dependent, T type, alpha 1I subunit

Example 3

A list/panel of 50 biomarkers (BioM50) was generated from the biomarkers with the best evidence from discovery, prioritization, validation, and testing in independent cohorts, obtained with additional data, longer follow-up, and longitudinal analyses (Table 23, FIG. 10 ).

In this Example, the following abbreviations were utilized: validation: DE—differential expression, AP—Absent/Present. NS—Non-stepwise; Step 4 Predictions: C—cross-sectional (using levels from one visit), L—longitudinal (using levels, slope, as well as maximum levels and maximum slope from multiple prior visits); M—Males, F—Females. MDD—depression, BP—bipolar, SZ—schizophrenia, SZA—schizoaffective, PSYCHOSIS—schizophrenia and schizoaffective combined, PTSD-post-traumatic stress disorder. In ALL, by Gender, and personalized by Gender and Diagnosis. Score for predictions: 4 pts if in ALL, 2 pts Gender, 1 pts Gender/Dx. Bold name genes are also Bonferroni significant at Step 3 validation.

To generate the BioM50, the raw gene expression data was first Z-scored by gender and diagnosis, for normalization purposes. Then, each of the biomarkers in the panel was multiplied by a weight coefficient corresponding to their CFE (convergent functional evidence) score, and then an additive score of the 50 weighted biomarkers was obtained. This score can be used for (1) objective assessment of suicidality state and (2) predictive purposes for future clinical worsening, as reflected in hospitalizations for suicidality. Two types of analyses can be performed: cross-sectional, and longitudinal (Table 23, FIGS. 11A-11C).

As depicted in FIGS. 11A-11C, for cross-sectional analyses, biomarker expression levels were used, z-scored by gender and diagnosis. For longitudinal analyses, four measures were combined: biomarker expression levels, slope (defined as ratio of levels at current testing visit vs. previous visit, divided by time between visits), maximum levels (at any of the current or past visits), and maximum slope (between any adjacent current or past visits). For decreased biomarkers, the minimum rather than the maximum was used for level calculations. All four measures were Z-scored, then combined in an additive fashion into a single measure. This type of longitudinal analysis can be carried out in patients that have at least two test visits.

The BioM-50 score of a new patient tested was compared against the scores of previously tested patients with known severity and outcomes. The thresholds were set based on averages of previous data, and on previous ROC AUC curves, choosing values for sensitivity and specificity. A report was generated with a raw score, a % score, and a risk classification (low, intermediate, high).

BioM50 scores can also be used in combination with quantitative phenotypic data from questionnaires/apps (such as CFI-S, SASS, others), in the UP-Suicide algorithm.

The biomarkers from the BioM50 panel can be used to (3) match patients to medications (Table 23, FIG. 12 ). Some biomarkers have corresponding known drugs or classes of drugs, that have an opposite effect to suicidality on their direction of change (pharmacogenomics). Such biomarkers can be used to target treatments to different patients, and to (4) measure response to that treatment. The higher the proportion/percentile of biomarkers for a certain drug/class, the more indicated that drug would be for treatment. When biomarkers for multiple different drug/classes are changed in an individual, a prioritization based on the proportion/percentile of biomarkers for each class can be used to choose the drug or combination of drugs (targeted rational polypharmacy).

The gene expression signature of the 50 biomarkers (BioM50) was used to identify repurposed drugs, for (5) new method of use in suicidality treatment and prevention (Table 24). The biological networks where these 50 biomarkers map offer additional targets for new drug development (FIG. 13 ).

For the top biomarkers identified, combining all the available evidence from this current Example and the published literature, into a convergent functional evidence (CFE) score (Table 23), leads to a prioritization of biomarkers for future studies in the field.

TABLE 23 CFE. Convergent Functional Evidence (CFE) Score: Prioritization of Top Biomarkers for Suicidality (resulting in a panel of n = 50 biomarkers, from 46 genes). Some genes have more than one biomarker probeset. The CFE score for each biomarker is based on the totality of evidence from our studies (Discovery, Prioritization, Validation, and Clinical Utility Testing). These biomarkers may be a panel, with the score for 50 biomarkers panel (BioM 50) computed in an additive way, with each biomarker in the panel having the CFE score as a weight coefficient. Discovery in Longitudinally Followed Patients Step 1 Validation in Discovery in Blood Step 2 Suicide Completers Gene (Direction of Change tracking Prioritization External CFG Step 3 Symbol/ High Suicidal Ideation) Evidence for Involvement in Validation Anova Gene Name Probeset Method/Score/% 4 pts Suicide Score 8 pt p-value 4 pts PSME4 237180_at (I) 4.00 3.81E−12 Proteasome DE/1 Bonferroni/4 Activator 46.2% Subunit 4 ACP1 201630_s_at (D) 6.00 4.03E−05 acid DE/2 Bonferroni/4 phosphatase 1, 55.2% soluble ACSL6 211207_s_at (D) 2.00 6.92E−02 acyl-CoA DE/4 Nominal/2 synthetase 94.8% long-chain family member 6 MAGI3 226770_at (D) 4.00 4.02E−12 membrane AP/2 Bonferroni/4 associated 56% guanylate kinase, WW and PDZ domain containing 3 PLPP3 212226_s_at (I) 4.00 1.65E−05 phospholipid DE/1 Bonferroni/4 phosphatase 3 36.9% (I) AP/2 53.1% SKA2 225686_at (D) 8.00 4.74E−03 spindle and AP/1 Nominal/2 kinetochore 34.5% associated complex subunit 2 SOD2 215078_at (I) 4.00 6.26E−11 superoxide DE/2 Bonferroni/4 dismutase 2, 73.8% mitochondrial CLN5 214252_s_at (D) 4.00 1.66E−11 ceroid- DE/2 Bonferroni/4 lipofuscinosis, 60.4% neuronal 5 CLTA 204050_s_at (D) 4.00 5.13E−07 clathrin, DE/2 Bonferroni/4 light 62.5% chain A DYRK2 202969_at (D) 4.00 2.29E−09 dual DE/2 Bonferroni/4 specificity 56.3% tyrosine-(Y)- phosphorylation regulated kinase 2 ECHDC1 223087_at (D) 4.00 2.12E−07 ethylmalonyl- DE/2 Bonferroni/4 CoA 74% decarboxylase 1 FBLN5 203088_at (D) 6.00 1.05E−11 fibulin 5 DE/2 Bonferroni/4 52.1% AIMP1 227605_at (D) 4.00 8.98E−13 aminoacyl tRNA DE/2 Bonferroni/4 synthetase 53.1% complex- (D) interacting AP/1 multifunctional 41.4% protein 1 CLN5 204084_s_at (D) 4.00 6.03E−15 ceroid- DE/1 Bonferroni/4 lipofuscinosis, 41.7% neuronal 5 ITGB1BP1 203336_s_at (D) 4.00 9.47E−06 integrin DE/2 Bonferroni/4 beta 1 57.3% binding protein 1 NR3C1 201866_s_at (D) 6.00 2.83E−06 nuclear DE/2 Bonferroni/4 receptor 53.1% subfamily 3, group C, member 1 (glucocorticoid receptor) PER1 244677_at (I) 4.00 3.52E−18 period DE/1 Bonferroni/4 circadian 37.7% clock 1 PIK3R1 244181_at (I) 4.00 7.33E−08 Phospho- DE/1 Bonferroni/4 inositide-3- 36.2% Kinase Regulatory Subunit 1 PRKAR2B 203680_at (D) 6.00 7.27E−06 protein DE/2 Bonferroni/4 kinase, 66.7% cAMP- dependent, regulatory, type II, beta SAE1 1555618_s_at (D) 0.00 3.33E−05 SUMO1 DE/4 Bonferroni/4 activating 86.5% enzyme subunit 1 SPATA18 229331_at (I) 4.00 1.39E−05 spermato- DE/2 Bonferroni/4 genesis 54.6% associated 18 ZNF565 228305_at (D) 4.00 3.43E−10 zinc finger DE/1 Bonferroni/4 protein 565 49% AIMP1 202542_s_at (D) 4.00 3.55E−05 aminoacyl tRNA DE/2 Bonferroni/4 synthetase 78.1% complex- interacting multifunctional protein 1 AIMP1 202541_at (D) 4.00 4.06E−05 aminoacyl tRNA DE/1 Bonferroni/4 synthetase 34.4% complex- interacting multifunctional protein 1 BCL2 203685_at (D) 6.00 1.55E−07 B-cell DE/2 Bonferroni/4 CLL/ 55.2% lymphoma 2 CAT 211922_s_at (D) 4.00 1.03E−08 catalase DE/2 Bonferroni/4 59.4% ECHDC1 219974_x_at (D) 4.00 2.94E−09 ethylmalonyl- DE/2 Bonferroni/4 CoA 59.4% decarboxylase 1 HDAC2 201833_at (D) 0.00 9.15E−08 histone DE/4 Bonferroni/4 deacetylase 2 82.3% LPP 241879_at (I) 4.00 8.45E−11 LIM domain DE/1 Bonferroni/4 containing 36.2% preferred translocation partner in lipoma PSMB4 202243_s_at (D) 6.00 5.97E−08 proteasome DE/2 Bonferroni/4 subunit 51% beta 4 RPE 221770_at (D) 2.00 2.79E−09 ribulose-5- DE/2 Bonferroni/4 phosphate- 68.8% 3-epimerase VTA1 223021_x_at (D) 4.00 1.01E−06 vesicle DE/2 Bonferroni/4 (multivesicular 52.1% body) trafficking 1 AKAP13 209534_x_at (I) 4.00 1.61E−07 A kinase DE/1 Bonferroni/4 (PRKA) 46.2% anchor protein 13 CD164 208654_s_at (D) 4.00 3.65E−07 CD164 DE/2 Bonferroni/4 molecule, 64.6% sialomucin CD47 211075_s_at (D) 4.00 6.65E−11 CD47 DE/2 Bonferroni/4 molecule 62.5% CYP4V2 226745_at (D) 2.00 6.31E−07 cytochrome DE/2 Bonferroni/4 P450, 50% family 4, subfamily V, polypeptide 2 DNAJC15 230305_at (D) 4.00 3.94E−08 DnaJ DE/2 Bonferroni/4 (Hsp40) 63.5% homolog, subfamily C, member 15 FNTA 209471_s_at (D) 0.00 2.15E−09 farnesyl- DE/4 Bonferroni/4 transferase, 90.6% CAAX box, alpha GIMAP4 219243_at (D) 2.00 1.90E−17 GTPase, DE/2 Bonferroni/4 IMAP 77.1% family member 4 GIMAP7 228071_at (D) 2.00 7.51E−08 GTPase, DE/2 Bonferroni/4 IMAP 71.9% family member 7 HACL1 223211_at (D) 4.00 8.93E−09 2-hydroxyacyl- DE/1 Bonferroni/4 CoA lyase 1 46.9% HNRNPA0 201054_at (D) 2.00 2.83E−10 heterogeneous DE/2 Bonferroni/4 nuclear 53.1% ribonucleo- protein A0 MRPS14 203801_at (D) 4.00 1.18E−11 mitochondrial DE/2 Bonferroni/4 ribosomal 50% protein S14 PIK3C3 232086_at (D) 3.00 1.43E−16 phosphatidyl- DE/2 Bonferroni/4 inositol 63.5% 3-kinase, catalytic subunit type 3 PRKCB 207957_s_at (D) 6.00 1.04E−11 protein DE/2 Bonferroni/4 kinase C, 51% beta PSMB1 214289_at (I) 6.00 2.51E−07 proteasome DE/1 Bonferroni/4 subunit 39.2% beta 1 (I) AP/2 54.7% SAT1 213988_s_at (I) 6.00 1.66E−20 spermidine/ DE/1 Bonferroni/4 spermine 39.2% N1- acetyltransferase 1 SLC6A4 241811_x_at (I) 8.00 NS solute carrier DE/2 family 6 70% (neurotransmitter transporter), member 4 TMEM245 223007_s_at (D) 4.00 1.89E−09 transmembrane DE/2 Bonferroni/4 protein 245 50% TPH2 1555332_at (I) 8.00 1.36E−01 tryptophan DE/1 Nominal/2 hydroxylase 2 33.8% Clinical Utility of our Biomarkers 1. Assessment of State, 2. Prediction of Future Risk, 3. Matching to Treatments Testing/Demonstration in Independent Clinical Cohorts Step 4 Step 4 Matching to Best Significant Step 4 Best Significant Treatments Predictions of State Best Significant Predictions of Trait (Pharmaco High Suicidal Predictions of Trait All Future Years Hosp genomics) Ideation First Year Hosp with with Suicidality Drugs that ROC AUC/ Suicidality OR/OR OR/OR Modulate the p-value 4 pts p-value 4 pts p-value 4 pts Biomarker Gene ALL 2 pts ALL 2 pts ALL 2 pts in opposite Symbol/ Gender 1 pts Gender 1 pts Gender 1 pts Direction to CFE Gene Name Gender/Dx Gender/Dx Gender/Dx Suicide Score PSME4 ALL ALL ALL Antidepressants 21 Proteasome C: C: C: Activator (54/320) (51/359) (140/477) Subunit 4 0.61/6.46E−03 0.64/9.99E−04 1.21/3.53E−03 Gender Gender L: Males Males (74/287) C: C: 1.31/3.89E−02 (46/247) (45/307) Gender 0.61/7.56E−03 0.65/8.54E−04 Females Gender Dx Gender Dx L: M-BP M-MDD (5/42) C: C: 6.08/4.17E−02 (12/82) (7/41) Gender 0.69/1.97E−02 0.72/3.19E−02 Males M-PTSD M-PTSD C: C: C: (129/409) (9/19) (6/24) 1.2/5.01E−03 0.79/1.69E−02 0.85/5.65E−03 Gender Dx M-BP C: (23/108) 1.33/4.02E−02 M-MDD C: (13/52) 1.55/3.03E−02 M-PTSD C: (12/28) 2.01/1.12E−02 M-SZA C: (37/99) 1.24/4.48E−02 M-SZA L: (19/57) 1.6/3.56E−02 ACP1 ALL ALL Omega-3 20 acid C: L: fatty acids phosphatase 1, (54/320) (74/287) Lithium soluble 0.63/1.77E−03 1.36/4.24E−02 Antidepressants Gender Gender Antipsychotics Males Males Psychotherapy C: L: (46/247) (69/245) 0.65/6.92E−04 1.44/2.21E−02 Gender Dx Gender Dx M-BP M-PTSD C: C: (12/82) (12/28) 0.74/4.69E−03 1.81/3.91E−02 M-PSYCHOSIS M-SZ C: L: (15/107) (17/62) 0.69/8.50E−03 1.94/3.46E−02 M-PTSD C: (9/19) 0.73/4.32E−02 M-PTSD L: (5/10) 0.92/1.41E−02 M-SZ L: (3/32) 0.79/4.96E−02 M-SZA C: (10/50) 0.69/3.45E−02 ACSL6 ALL ALL ALL 20 acyl-CoA C: C: C: synthetase (54/320) (51/359) (140/477) long-chain 0.6/1.17E−02 0.59/2.50E−02 1.26/1.28E−02 family Gender Gender Gender member 6 Males Males Males C: C: C: (46/247) (45/307) (129/409) 0.65/1.04E−03 0.59/2.40E−02 1.26/1.57E−02 Gender Dx Gender Dx M-BP M-BP C: C: (12/82) (23/108) 0.79/6.84E−04 2.72/3.09E−02 M-PSYCHOSIS C: (15/107) 0.63/4.94E−02 M-PTSD C: (9/19) 0.84/5.68E−03 MAGI3 ALL Gender ALL Antipsychotics 20 membrane C: Males C: associated (54/320) C: (140/477) guanylate 0.6/1.30E−02 (45/307) 1.26/5.13E−03 kinase, WW Gender 0.58/4.79E−02 L: and PDZ Males Gender Dx (74/287) domain C: M-PSYCHOSIS 1.44/1.47E−02 containing 3 (46/247) C: Gender 0.61/9.81E−03 (21/134) Males L: 0.65/1.39E−02 C: (16/133) M-SZ (129/409) 0.64/3.34E−02 C: 1.35/1.04E−03 Gender Dx (12/67) L: M-BP 0.66/3.87E−02 (69/245) C: 1.52/7.94E−03 (12/82) Gender Dx 0.68/2.38E−02 M-PSYCHOSIS M-PSYCHOSIS C: C: (68/200) (15/107) 1.69/2.39E−04 0.78/3.04E−04 M-PSYCHOSIS M-PSYCHOSIS L: L: (36/119) (6/56) 1.6/2.71E−02 0.72/4.25E−02 M-SZ M-SZ C: C: (31/101) (5/57) 1.82/3.27E−03 0.79/1.60E−02 M-SZ M-SZ L: L: (17/62) (3/32) 2.46/1.53E−02 0.91/1.09E−02 M-SZA M-SZA C: C: (37/99) (10/50) 1.52/2.15E−02 0.78/3.30E−03 PLPP3 ALL Gender ALL 20 phospholipid C: Males C: phosphatase 3 (54/320) C: (140/477) 0.58/3.75E−02 (45/307) 1.17/1.50E−02 Gender 0.59/2.86E−02 Gender Males Gender Dx Males C: M-BP C: (46/247) C: (129/409) 0.59/2.61E−02 (8/92) 1.22/2.90E−03 Gender Dx 0.73/1.76E−02 Gender Dx M-BP M- M-BP C: PSYCHOSIS C: (12/82) C: (23/108) 0.68/2.69E−02 (21/134) 1.4/1.85E−02 M-PSYCHOSIS 0.61/4.83E−02 M-PSYCHOSIS C: M-SZA C: (15/107) C: (68/200) 0.65/3.43E−02 (9/67) 1.18/4.01E−02 0.69/3.73E−02 M-SZA C: (37/99) 1.28/1.73E−02 SKA2 ALL Gender Dx ALL 20 spindle and C: M-SZA C: kinetochore (54/320) C: (140/477) associated 0.61/6.70E−03 (9/67) 1.17/4.49E−02 complex Gender 0.71/1.97E−02 Gender subunit 2 Males Males C: C: (46/247) (129/409) 0.65/8.03E−04 1.22/2.39E−02 Gender Dx Gender Dx M-BP M-BP C: C: (12/82) (23/108) 0.68/2.61E−02 2.05/1.67E−02 M-MDD L: (2/14) 0.92/3.39E−02 M-PSYCHOSIS C: (15/107) 0.74/1.77E−03 M-PSYCHOSIS L: (6/56) 0.72/4.02E−02 M-SZ C: (5/57) 0.79/1.60E−02 M-SZ L: (3/32) 0.86/2.09E−02 M-SZA C: (10/50) 0.7/2.77E−02 SOD2 Gender ALL ALL Antidepressants 20 superoxide Males C: C: Antipsychotics dismutase 2, C: (51/359) (140/477) mitochondrial (46/247) 0.6/1.43E−02 1.24/3.68E−03 0.58/4.93E−02 Gender Gender Gender Dx Males Females M-PSYCHOSIS C: L: C: (45/307) (5/42) (15/107) 0.61/8.62E−03 3.28/3.25E−02 0.66/2.37E−02 Gender Dx Gender M-BP Males C: C: (8/92) (129/409) 0.68/4.96E−02 1.25/3.21E−03 M-PTSD Gender Dx C: M-BP (6/24) C: 0.75/3.59E−02 (23/108) 1.47/1.95E−02 CLN5 ALL Gender Dx ALL 19 ceroid- C: M-SZA C: lipofuscinosis, (54/320) C: (140/477) neuronal 5 0.64/4.17E−04 (9/67) 1.23/9.78E−03 Gender 0.68/4.03E−02 L: Males (74/287) C: 1.4/2.71E−02 (46/247) Gender 0.66/2.71E−04 Males Gender Dx C: M-BP (129/409) C: 1.27/5.59E−03 (12/82) L: 0.74/4.02E−03 (69/245) M-PSYCHOSIS 1.49/1.39E−02 C: Gender Dx (15/107) M-BP 0.71/4.39E−03 C: M-PSYCHOSIS (23/108) L: 1.65/2.60E−02 (6/56) 0.71/4.76E−02 M-SZ C: (5/57) 0.73/4.53E−02 M-SZ L: (3/32) 0.83/3.27E−02 M-SZA C: (10/50) 0.72/1.85E−02 CLTA ALL Gender Dx ALL Antipsychotics 19 clathrin, C: M-SZA L: light (54/320) C: (74/287) chain A 0.59/2.08E−02 (9/67) 1.3/4.49E−02 Gender 0.68/4.54E−02 Gender Males Males C: L: (46/247) (69/245) 0.6/1.77E−02 1.33/3.35E−02 Gender Dx M-BP C: (12/82) 0.71/1.01E−02 M-PSYCHOSIS C: (15/107) 0.68/1.40E−02 M-SZA C: (10/50) 0.69/3.45E−02 DYRK2 ALL Gender Dx ALL Antipsychotics 19 dual C: M-PTSD L: specificity (54/320) C: (74/287) tyrosine-(Y)- 0.6/7.73E−03 (6/24) 1.39/2.99E−02 phosphorylation L: 0.78/2.28E−02 Gender regulated (17/174) Males kinase 2 0.62/4.85E−02 L: Gender (69/245) Males 1.46/1.67E−02 C: Gender Dx (46/247) M-PTSD 0.64/1.34E−03 C: L: (12/28) (16/133) 2.08/2.36E−02 0.66/2.10E−02 L: Gender Dx (8/16) M-BP 2.73/3.54E−02 C: M-SZ (12/82) L: 0.73/5.26E−03 (17/62) M-PSYCHOSIS 1.81/4.16E−02 C: (15/107) 0.73/2.49E−03 L: (6/56) 0.74/2.82E−02 M-SZ C: (5/57) 0.73/4.26E−02 Gender Dx M-SZ L: (3/32) 0.89/1.52E−02 Gender Dx M-SZA C: (10/50) 0.74/1.13E−02 ECHDC1 ALL Gender Dx ALL 19 ethylmalonyl- C: M-PTSD C: CoA (54/320) C: (140/477) decarboxylase 1 0.62/2.09E−03 (6/24) 1.18/3.14E−02 Gender 0.76/3.10E−02 Gender Males Males C: C: (46/247) (129/409) 0.64/1.49E−03 1.18/3.75E−02 Gender Dx Gender Dx M-BP M-PTSD C: C: (12/82) (12/28) 0.68/2.38E−02 2.14/2.62E−02 M-PSYCHOSIS C: (15/107) 0.67/1.53E−02 M-SZ L: (3/32) 0.82/3.77E−02 M-SZA C: (10/50) 0.71/2.34E−02 FBLN5 Gender Dx ALL Gender 19 fibulin 5 M-SZA C: Males C: (51/359) C: (10/50) 0.6/1.13E−02 (129/409) 0.69/3.45E−02 Gender 1.21/1.96E−02 Males Gender C: Males (45/307) L: 0.64/1.50E−03 (69/245) Gender Dx 1.45/1.62E−02 M-PSYCHOSIS Gender Dx C: M-PSYCHOSIS (21/134) C: 0.65/1.50E−02 (68/200) M-PTSD 1.36/1.00E−02 C: L: (6/24) (36/119) 0.73/4.78E−02 1.68/1.04E−02 M-SZ M-SZ L: C: (5/36) (31/101) 0.74/4.31E−02 1.46/3.22E−02 M-SZ L: (17/62) 2.17/1.36E−02 AIMP1 ALL ALL 18 aminoacyl tRNA C: C: synthetase (54/320) (140/477) complex- 0.62/2.41E−03 1.17/3.79E−02 interacting L: Gender multifunctional (17/174) Males protein 1 0.63/3.58E−02 C: Gender (129/409) Males 1.22/1.86E−02 C: Gender Dx (46/247) M-BP 0.67/2.25E−04 C: L: (23/108) (16/133) 1.44/5.00E−02 0.65/2.36E−02 M-PSYCHOSIS Gender Dx C: M-BP (68/200) C: 1.3/2.48E−02 (12/82) M-SZ 0.73/5.06E−03 C: M-PSYCHOSIS (31/101) C: 1.46/4.63E−02 (15/107) 0.71/5.55E−03 M-PTSD L: (5/10) 0.92/1.41E−02 M-SZA C: (10/50) 0.76/6.24E−03 CLN5 ALL Gender Dx ALL 18 ceroid- C: M-PSYCHOSIS C: lipofuscinosis, (54/320) C: (140/477) neuronal 5 0.62/3.63E−03 (21/134) 1.22/1.37E−02 Gender 0.63/2.91E−02 L: Males M-SZA (74/287) C: C: 1.36/3.95E−02 (46/247) (9/67) Gender 0.63/2.17E−03 0.76/5.89E−03 Males Gender Dx C: M-BP (129/409) C: 1.26/5.59E−03 (12/82) L: 0.72/7.61E−03 (69/245) M-PSYCHOSIS 1.43/2.18E−02 C: Gender Dx (15/107) M-PSYCHOSIS 0.7/6.31E−03 C: M-PSYCHOSIS (68/200) L: 1.34/9.29E−03 (6/56) L: 0.71/4.76E−02 (36/119) M-SZ 1.63/2.07E−02 L: M-SZ (3/32) L: 0.84/2.82E−02 (17/62) M-SZA 2.14/1.66E−02 C: M-SZA (10/50) C: 0.75/7.65E−03 (37/99) 1.43/1.72E−02 ITGB1BP1 ALL ALL Lithium 18 integrin C: C: beta 1 (54/320) (140/477) binding 0.57/4.27E−02 1.26/3.93E−03 protein 1 Gender L: Males (74/287) C: 1.51/6.20E−03 (46/247) Gender 0.61/1.17E−02 Males C: (129/409) 1.31/1.49E−03 L: (69/245) 1.61/3.09E−03 Gender Dx M-PSYCHOSIS C: (68/200) 1.28/2.19E−02 M-SZA C: (37/99) 2.17/1.06E−04 L: (19/57) 1.8/2.44E−02 NR3C1 ALL Gender Valproate 18 nuclear C: Males Antidepressants receptor (54/320) L: Antipsychotics subfamily 0.58/4.00E−02 (69/245) 3, group C, Gender 1.38/3.05E−02 member 1 Males (glucocorticoid C: receptor) (46/247) 0.58/4.91E−02 Gender Dx F-MDD C: (2/11) 0.89/4.95E−02 M-BP C: (12/82) 0.69/1.91E−02 PER1 ALL Gender Dx ALL Antidepressants 18 period C: M-PSYCHOSIS C: Anxiolytics circadian (54/320) C: (140/477) clock 1 0.62/3.51E−03 (21/134) 1.16/2.89E−02 Gender 0.64/1.94E−02 L: Females (74/287) C: 1.49/3.57E−03 (8/73) Gender 0.75/1.19E−02 Males Gender C: Males (129/409) C: 1.15/4.75E−02 (46/247) L: 0.6/2.19E−02 (69/245) Gender Dx 1.53/3.28E−03 F-MDD Gender Dx C: M-PSYCHOSIS (2/11) C: 1/1.69E−02 (68/200) M-BP 1.3/7.61E−03 C: L: (12/82) (36/119) 0.68/2.24E−02 1.52/1.87E−02 M-SZ Gender Dx C: M-SZ (5/57) C: 0.73/4.80E−02 (31/101) 1.43/1.26E−02 PIK3R1 Gender Dx ALL ALL Lithium 18 Phospho- M-PTSD C: L: Psychotherapy inositide-3- C: (51/359) (74/287) Kinase (9/19) 0.58/4.10E−02 1.27/4.23E−02 Regulatory 0.76/3.02E−02 Gender Gender Dx Subunit 1 Females F-MDD L: C: (1/31) (3/17) 1/4.68E−02 2.93/3.98E−02 Gender Dx M-PTSD F-MDD C: C: (12/28) (3/17) 1.7/2.41E−02 0.91/1.78E−02 M-PTSD L: (8/16) 1.94/4.50E−02 PRKAR2B Gender Dx Gender Dx ALL Valproate 18 protein F-BP M-BP L: Antipsychotics kinase, C: C: (74/287) cAMP- (3/32) (8/92) 1.44/2.15E−02 dependent, 0.84/2.82E−02 0.68/4.55E−02 Gender regulatory, Gender Dx Males type II, M-BP L: beta L: (69/245) (3/57) 1.4/3.74E−02 0.8/4.00E−02 Gender Dx M-BP C: (23/108) 1.63/2.48E−02 L: (11/68) 3.34/4.09E−02 SAE1 Gender ALL ALL 18 SUMO1 Females C: C: activating C: (51/359) (140/477) enzyme (8/73) 0.58/3.97E−02 1.17/3.25E−02 subunit 1 0.71/2.60E−02 Gender Gender Gender Dx Males Males F-MDD C: C: C: (45/307) (129/409) (2/11) 0.58/4.26E−02 1.2/1.82E−02 0.89/4.95E−02 Gender Dx Gender Dx M-BP M-MDD C: C: (8/92) (13/52) 0.68/4.29E−02 2.4/1.56E−03 M-MDD M-MDD C: L: (7/41) (6/29) 0.7/4.81E−02 2.76/2.76E−02 SPATA18 ALL ALL 18 spermatogenesis C: L: associated 18 (54/320) (19/200) 0.59/2.23E−02 0.62/3.92E−02 L: Gender Dx (17/174) M-PSYCHOSIS 0.65/2.04E−02 L: Gender (7/70) Males 0.77/9.66E−03 C: M-SZ (46/247) L: 0.58/3.76E−02 (5/36) L: 0.88/3.73E−03 (16/133) 0.63/4.78E−02 Gender Dx M-PSYCHOSIS L: (6/56) 0.72/4.25E−02 M-PTSD L: (5/10) 0.84/3.79E−02 ZNF565 ALL Gender Dx ALL 18 zinc finger C: M-SZA C: protein 565 (54/320) C: (140/477) 0.58/4.07E−02 (9/67) 1.18/2.99E−02 Gender 0.7/2.68E−02 L: Males (74/287) C: 1.34/4.22E−02 (46/247) Gender 0.61/1.04E−02 Males Gender Dx C: M-PSYCHOSIS (129/409) C: 1.21/1.67E−02 (15/107) L: 0.71/4.88E−03 (69/245) M-SZA 1.44/1.88E−02 C: Gender Dx (10/50) M-PSYCHOSIS 0.75/8.17E−03 C: (68/200) 1.23/4.28E−02 L: (36/119) 1.51/3.57E−02 M-SZ L: (17/62) 1.91/2.16E−02 M-SZA C: (37/99) 1.46/2.62E−02 AIMP1 ALL Gender Dx Gender 17 aminoacyl tRNA C: M-SZA Males synthetase (54/320) C: C: complex- 0.6/1.20E−02 (9/67) (129/409) interacting Gender 0.69/3.17E−02 1.19/3.60E−02 multifunctional Males L: protein 1 C: (69/245) (46/247) 1.36/4.76E−02 0.63/2.43E−03 Gender Dx Gender Dx M-BP M-PSYCHOSIS C: C: (23/108) (15/107) 1.77/2.21E−02 0.72/2.86E−03 M-PTSD M-SZ C: L: (12/28) (3/32) 1.8/4.33E−02 0.84/2.82E−02 M-SZA C: (10/50) 0.74/9.32E−03 AIMP1 ALL ALL 17 aminoacyl tRNA C: C: synthetase (54/320) (140/477) complex- 0.62/2.92E−03 1.2/2.64E−02 interacting Gender L: multifunctional Males (74/287) protein 1 C: 1.36/4.65E−02 (46/247) Gender 0.65/6.19E−04 Males Gender Dx C: M-BP (129/409) C: 1.25/1.31E−02 (12/82) L: 0.68/2.10E−02 (69/245) M-PSYCHOSIS 1.41/3.38E−02 C: Gender Dx (15/107) M-PSYCHOSIS 0.69/1.03E−02 C: M-SZ (68/200) L: 1.38/1.31E−02 (3/32) M-SZA 0.82/3.77E−02 C: M-SZA (37/99) C: 1.51/1.70E−02 (10/50) 0.7/2.47E−02 BCL2 ALL Gender Dx Lithium 17 B-cell C: M-SZ Valproate CLL/ (54/320) C: Antipsychotics lymphoma 2 0.64/7.17E−04 (31/101) Gender 1.37/4.28E−02 Males C: (46/247) 0.65/5.80E−04 Gender Dx M-BP C: (12/82) 0.74/4.69E−03 M-PSYCHOSIS C: (15/107) 0.69/8.50E−03 M-SZ C: (5/57) 0.78/2.11E−02 L: (3/32) 0.85/2.43E−02 CAT ALL Gender Gender Dx 17 catalase C: Males M-MDD (54/320) C: C: 0.62/2.24E−03 (45/307) (13/52) Gender 0.58/4.90E−02 2.02/1.68E−02 Females Gender Dx C: M-MDD (8/73) C: 0.73/1.70E−02 (7/41) Gender 0.72/3.58E−02 Males M-SZA C: C: (46/247) (9/67) 0.6/1.58E−02 0.72/1.65E−02 Gender Dx F-MDD C: (2/11) 0.94/2.97E−02 M-BP C: (12/82) 0.75/3.44E−03 ECHDC1 ALL Gender Dx Gender 17 ethylmalonyl- C: M-SZA Males CoA (54/320) C: C: decarboxylase 1 0.61/4.99E−03 (9/67) (129/409) Gender 0.7/2.91E−02 1.18/3.76E−02 Males L: C: (69/245) (46/247) 1.41/2.93E−02 0.61/9.52E−03 Gender Dx M-BP C: (12/82) 0.67/2.94E−02 M-SZA C: (10/50) 0.67/4.95E−02 HDAC2 ALL Gender Dx ALL Lithium 17 histone C: M-PTSD L: deacetylase 2 (54/320) C: (74/287) 0.64/6.78E−04 (6/24) 1.38/2.95E−02 Gender 0.75/3.59E−02 Gender Males Males C: L: (46/247) (69/245) 0.64/1.10E−03 1.45/1.73E−02 Gender Dx Gender Dx M-BP M-BP C: C: (12/82) (23/108) 0.71/9.10E−03 1.6/1.61E−02 M-PSYCHOSIS C: (15/107) 0.68/1.13E−02 M-SZA C: (10/50) 0.67/4.71E−02 LPP ALL Gender Gender 17 LIM domain C: Females Females containing (54/320) L: L: preferred 0.62/2.14E−03 (1/31) (5/42) translocation Gender 1/4.68E−02 3.02/3.56E−02 partner Females Gender Dx Gender Dx in lipoma C: M-PTSD F-MDD (8/73) C: C: 0.72/2.20E−02 (6/24) (3/17) Gender 0.82/9.82E−03 3.33/3.37E−02 Males M-MDD C: L: (46/247) (6/29) 0.61/1.07E−02 2.21/3.20E−02 Gender Dx M-PTSD F-BP C: C: (12/28) (3/32) 1.92/7.27E−03 0.84/2.82E−02 M-PTSD C: (9/19) 0.74/3.62E−02 PSMB4 ALL Gender Dx Benzodiazepines 17 proteasome C: M-SZA subunit (54/320) C: beta 4 0.59/1.87E−02 (9/67) Gender 0.7/3.04E−02 Males C: (46/247) 0.63/2.91E−03 Gender Dx M-BP C: (12/82) 0.7/1.50E−02 M-PSYCHOSIS C: (15/107) 0.71/4.63E−03 M-SZA C: (10/50) 0.76/5.44E−03 RPE ALL Gender Dx ALL 17 ribulose-5- C: M-PTSD L: phosphate- (54/320) C: (74/287) 3-epimerase 0.6/1.15E−02 (6/24) 1.4/3.01E−02 Gender 0.91/1.68E−03 Gender Males Gender Dx Males C: M-PTSD L: (46/247) L: (69/245) 0.62/5.61E−03 (4/13) 1.45/2.37E−02 Gender Dx 0.89/1.54E−02 Gender Dx M-BP M-PTSD C: C: (12/82) (12/28) 0.7/1.47E−02 3.51/6.74E−03 M-PSYCHOSIS L: C: (8/16) (15/107) 3.93/8.53E−03 0.66/2.50E−02 M-SZ L: (3/32) 0.84/2.82E−02 VTA1 ALL Gender Dx Gender 17 vesicle C: M-SZA Males (multivesicular (54/320) C: L: body) 0.6/1.26E−02 (9/67) (69/245) trafficking 1 Gender 0.72/1.72E−02 1.43/3.26E−02 Males C: (46/247) 0.61/1.00E−02 Gender Dx M-BP C: (12/82) 0.68/2.31E−02 M-PSYCHOSIS C: (15/107) 0.68/1.19E−02 M-SZ L: (3/32) 0.84/2.82E−02 M-SZA C: (10/50) 0.72/1.74E−02 AKAP13 Gender Dx Gender ALL Antipsychotics 16 A kinase M-PTSD Females L: (PRKA) C: L: (74/287) anchor (9/19) (1/31) 1.3/2.13E−02 protein 13 0.76/3.02E−02 1/4.68E−02 Gender Gender Dx Females M-PTSD L: C: (5/42) (6/24) 3.36/2.31E−02 0.78/2.28E−02 Gender Males L: (69/245) 1.26/4.34E−02 Gender Dx M-PTSD C: (12/28) 1.67/3.09E−02 CD164 ALL Gender Dx Gender Dx Antipsychotics 16 CD164 C: M-PTSD M-PTSD molecule, (54/320) C: C: sialomucin 0.61/3.94E−03 (6/24) (12/28) Gender 0.81/1.39E−02 2.15/1.91E−02 Males C: (46/247) 0.62/5.70E−03 Gender Dx M-BP C: (12/82) 0.72/7.34E−03 M-SZ L: (3/32) 0.82/3.77E−02 CD47 ALL Gender Dx Gender Dx Omega-3 16 CD47 C: M-SZA M-PTSD fatty acids molecule (54/320) C: C: Antipsychotics 0.6/1.03E−02 (9/67) (12/28) Gender 0.68/4.54E−02 1.87/3.94E−02 Males C: (46/247) 0.63/2.94E−03 M-BP C: (12/82) 0.67/3.22E−02 M-PSYCHOSIS C: (15/107) 0.69/7.89E−03 M-SZ L: (3/32) 0.8/4.33E−02 M-SZA C: (10/50) 0.74/9.32E−03 CYP4V2 ALL ALL Antidepressants 16 cytochrome C: C: P450, (54/320) (140/477) family 4, 0.57/4.20E−02 1.25/8.55E−03 subfamily V, Gender Gender polypeptide 2 Males Males C: C: (46/247) (129/409) 0.61/1.14E−02 1.26/7.94E−03 Gender Dx Gender Dx M-BP M-BP C: C: (12/82) (23/108) 0.77/1.58E−03 1.68/2.19E−02 M-PSYCHOSIS M-PSYCHOSIS C: C: (15/107) (68/200) 0.68/1.36E−02 1.32/2.05E−02 M-SZA M-SZA C: C: (10/50) (37/99) 0.78/3.82E−03 1.42/2.59E−02 DNAJC15 ALL Gender Dx Gender Dx 16 DnaJ C: M-PTSD M-PTSD (Hsp40) (54/320) C: C: homolog, 0.57/4.69E−02 (6/24) (12/28) subfamily Gender 0.76/2.87E−02 2.37/2.03E−02 C, member 15 Males C: (46/247) 0.59/2.93E−02 Gender Dx M-PTSD C: (9/19) 0.77/2.27E−02 FNTA ALL ALL 16 farnesyl- C: L: transferase, (54/320) (74/287) CAAX 0.6/9.25E−03 1.35/4.46E−02 box, Gender Gender alpha Males Males C: L: (46/247) (69/245) 0.63/3.64E−03 1.43/2.51E−02 Gender Dx M-BP C: (12/82) 0.74/4.52E−03 M-PSYCHOSIS C: (15/107) 0.65/3.10E−02 M-SZ L: (3/32) 0.83/3.27E−02 GIMAP4 ALL ALL Benzodiazepines 16 GTPase, C: C: IMAP (54/320) (140/477) family 0.6/8.98E−03 1.19/1.94E−02 member 4 Gender L: Males (74/287) C: 1.49/1.00E−02 (46/247) Gender 0.62/4.62E−0 Males Gender Dx C: M-BP (129/409) C: 1.21/1.57E−02 (12/82) L: 0.73/5.67E−03 (69/245) 1.55/5.93E−03 Gender Dx M-PTSD L: (8/16) 2.45/3.52E−02 GIMAP7 ALL ALL 16 GTPase, C: C: IMAP (54/320) (140/477) family 0.67/3.59E−05 1.22/1.48E−02 member 7 Gender Gender Males Males C: C: (46/247) (129/409) 0.7/1.36E−05 1.23/1.55E−02 Gender Dx Gender Dx M-BP M-BP C: C: (12/82) (23/108) 0.78/1.22E−03 1.54/3.90E−02 M-PSYCHOSIS M-PSYCHOSIS C: C: (15/107) (68/200) 0.66/2.08E−02 1.27/3.91E−02 M-PTSD M-PTSD C: L: (9/19) (8/16) 0.84/5.68E−03 2.42/3.55E−02 M-SZ L: (3/32) 0.86/2.09E−02 HACL1 Gender Gender Dx ALL 16 2-hydroxyacyl- Males M-SZA C: CoA lyase 1 C: C: (140/477) (46/247) (9/67) 1.19/2.11E−02 0.62/6.04E−03 0.68/3.88E−02 L: Gender Dx (74/287) M-BP 1.35/3.32E−02 C: Gender (12/82) Males 0.66/3.83E−02 C: M-PSYCHOSIS (129/409) C: 1.24/8.47E−03 (15/107) L: 0.72/2.70E−03 (69/245) M-SZA 1.42/1.71E−02 C: Gender Dx (10/50) M-PSYCHOSIS 0.76/6.68E−03 C: (68/200) 1.26/3.24E−02 M-SZ L: (17/62) 1.92/3.45E−02 HNRNPA0 ALL ALL 16 heterogeneous C: L: nuclear (54/320) (74/287) ribonucleo- 0.6/9.17E−03 1.35/4.70E−02 protein A0 Gender Gender Males Males C: L: (46/247) (69/245) 0.61/9.34E−03 1.38/3.73E−02 Gender Dx M-BP C: (12/82) 0.75/3.18E−03 M-PSYCHOSIS C: (15/107) 0.71/5.55E−03 M-SZ C: (5/57) 0.75/3.34E−02 M-SZ L: (3/32) 0.79/4.96E−02 M-SZA C: (10/50) 0.69/3.45E−02 MRPS14 ALL Gender Omega-3 16 mitochondrial C: Males fatty acids ribosomal (54/320) C: protein S14 0.61/6.26E−03 (129/409) Gender 1.2/3.06E−02 Males Gender C: Males (46/247) L: 0.64/1.76E−03 (69/245) Gender Dx 1.41/2.99E−02 M-BP Gender Dx C: M-BP (12/82) C: 0.72/8.78E−03 (23/108) M-PSYCHOSIS 1.51/4.23E−02 C: (15/107) 0.71/4.51E−03 M-SZ C: (5/57) 0.73/4.80E−02 L: (3/32) 0.79/4.96E−02 M-SZA C: (10/50) 0.71/1.96E−02 PIK3C3 ALL Gender Dx Gender Antidepressants 16 phosphatidyl- C: M-PTSD Males inositol (54/320) C: L: 3-kinase, 0.58/3.62E−02 (6/24) (69/245) catalytic Gender Dx 0.83/8.20E−03 1.38/3.36E−02 subunit F-MDD Gender Dx Gender Dx type 3 C: M-SZA M-PSYCHOSIS (2/11) C: L: 0.94/2.97E−02 (9/67) (36/119) M-BP 0.7/2.79E−02 1.57/2.66E−02 C: M-PTSD (12/82) C: 0.65/4.92E−02 (12/28) 1.94/3.19E−02 PRKCB ALL Lithium 16 protein C: kinase C, (54/320) beta 0.61/3.96E−03 Gender Males C: (46/247) 0.61/8.52E−03 Gender Dx M-BP C: (12/82) 0.76/2.21E−03 PSMB1 Gender Gender Dx Gender Dx 16 proteasome Females F-MDD M-BP subunit C: C: L: beta 1 (8/73) (3/17) (11/68) 0.75/1.19E−02 0.88/2.58E−02 1.94/2.90E−02 Gender Dx M-SZA F-MDD L: C: (19/57) (2/11) 1.56/3.41E−02 1/1.69E−02 M-PTSD C: (9/19) 0.8/1.37E−02 L: (5/10) 0.92/1.41E−02 SAT1 ALL Gender Dx Omega-3 16 spermidine/ C: M-SZ fatty acids spermine (51/359) C: N1- 0.59/1.62E−02 (31/101) acetyltransferase 1 Gender 1.43/2.42E−02 Males C: (45/307) 0.59/3.02E−02 Gender Dx M-SZ C: (12/67) 0.68/2.58E−02 SLC6A4 ALL Gender Omega-3 16 solute C: Females fatty acids carrier (54/320) C: Lithium family 6 0.63/1.73E−03 (11/68) Antidepressants (neurotransmitter Gender 1.94/2.25E−02 Remifentanil transporter), Males Exposure member 4 C: therapy (46/247) 0.66/3.89E−04 M-BP C: (12/82) 0.7/1.57E−02 M-PSYCHOSIS C: (15/107) 0.67/1.56E−02 M-SZ C: (5/57) 0.77/2.42E−02 TMEM245 Gender ALL 16 transmembrane Males C: protein 245 C: (140/477) (45/307) 1.2/1.50E−02 0.58/4.98E−02 Gender Gender Dx Males M-BP C: C: (129/409) (8/92) 1.21/1.71E−02 0.72/2.08E−02 TPH2 ALL Gender Dx Antipsychotics 16 tryptophan C: M-BP Physical and hydroxylase 2 (54/320) C: Cognitive 0.65/2.98E−04 (23/108) stimulation Gender 1.36/4.64E−02 Males C: (46/247) 0.68/6.60E−05 Gender Dx M-BP C: (12/82) 0.89/7.93E−06 M-PSYCHOSIS C: (15/107) 0.69/8.29E−03 M-SZA C: (10/50) 0.75/8.17E−03

TABLE 24 New drug Discovery/Repurposing. A. Top CFE BioM 50 Connectivity Map (CMAP) database discovery. Query for signature was done using exact Affymetrix probesets and direction of change. Drugs that have opposite gene expression profile effects to suicidality biomarkers signatures. A score of −1 indicates the perfect match, i.e. the best potential therapeutic for treating suicide. B. Top CFE BioM 50 NIH LINCS database discovery. Using the L1000CDS2 (LINCS L1000 Characteristic Direction Signature Search Engine) tool. Query for signature was done using gene symbols and direction of change. Shown are compounds Reversing direction of change in suicidality. A. Top CFE BioM 50 CMAP Discovery (n = 46 unique genes; 5 increased and 25 decreased were present in HG-U133A array used by CMAP) Rank CMAP name Score 1 trimethoprim −1 2 ethoxyquin −0.979 3 haloperidol −0.966 4 terazosin −0.947 5 pepstatin −0.921 6 diethylstilbestrol −0.919 7 nifenazone −0.905 8 metrizamide −0.902 9 prazosin −0.87 10 baclofen −0.864 B. Top CFE BioM 50 LINCS Discovery (n = 46 unique genes; 12 increased and 34 decreased). Rank Drug Score 1 Daunorubicin hydrochloride 0.1143 2 BRD-K06666320 0.1143 3 WZ-3105 0.1143 4 Piretanide 0.0857 5 Syk Inhibitor 0.0857 6 vorinostat 0.0857 7 DACTINOMYCIN 0.0857 8 trichostatin A 0.0857 9 Tiotidine 0.0857 10 troglitazone 0.0857

In view of the above, it will be seen that the several advantages of the disclosure are achieved and other advantageous results attained. As various changes could be made in the above methods without departing from the scope of the disclosure, it is intended that all matter contained in the above description and shown in the accompanying drawings shall be interpreted as illustrative and not in a limiting sense.

When introducing elements of the present disclosure or the various versions, embodiment(s) or aspects thereof, the articles “a”, “an”, “the” and “said” are intended to mean that there are one or more of the elements. The terms “comprising”, “including” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. 

What is claimed is:
 1. A method for treating suicidality and mitigating suicidality risk in a subject in need thereof, comprising the steps of: determining an expression level of at least a fist panel of blood biomarkers or a second panel of blood biomarkers in a sample from the subject; wherein the first panel of biomarkers in the panel comprise; Apolipoprotein E (APOE), anchor protein 13 (AKAP13), aldehyde dehydrogenase 7 family, member A1 (ALDH7A1), adipogenesis regulatory factor (ADIRF), beta-2-microglobulin (B2M), Gene Accession No. BF114768, CD109 molecule (CD109), Cortactin (CTTN), chromosome 14 open reading frame 180 (C14ORF180), Dab, mitogen-responsive phosphoprotein, homolog 2 (Drosophila) (DAB2), dual specificity phosphatase 13 (DUSP13), EGF containing fibulin-like extracellular matrix protein 2 (EFEMP2), fatty acid desaturase 1 (FADS1), GRB2 Associated Binding Protein 1 (GAB1), glycine amidinotransferase (L-arginine: glycine amidinotransferase) (GATM), 5-Hydroxytryptamine Receptor 2A (HTR2A), Histone Cluster 1 H2B Family Member O (HIST1H2BO), Interleukin 6 (IL6), interleukin 13 (IL13), inositol-trisphosphate 3-kinase B (ITPKB), Lipoma HMGIC fusion partner (LHFP), LIM domain containing preferred translocation partner in lipoma (LPP), metallothionein 1E (MT1E), Nerve Growth Factor Receptor (NGFR), Proteasome Activator Subunit 4 (PSME4), proteasome subunit beta 1 (PSMB1), phospholipid phosphatase 3 (PLPP3), period circadian clock 1 (PER1), Phosphoinositide-3-Kinase Regulatory Subunit 1 (PIK3R1), phosphatidic acid phosphatase type 2B (PPAP2B), protein tyrosine kinase 2 (PTK2), spermidine/spermine N1-acetyltransferase 1 (SAT1), septin 8 SEPT8, solute carrier family 4 (sodium bicarbonate cotransporter), member (SLC4A4), superoxide dismutase 2, mitochondrial (SOD2), spermatogenesis associated 18 (SPATA18), synaptopodin 2-like (SYNPO2L), Transmembrane 4 L Six Family Member 1 (TM4SF1), tryptophan hydroxylase 2 (TPH2), and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein (YWHAH), wherein the second panel of biomarkers comprises acid phosphatase 1, soluble (ACP1), acyl-CoA synthetase long-chain family member 6 (ACSL6), adenylate kinase 2 (AK2), arrestin, beta (ARRB1), aminoacyl tRNA synthetase complex-interacting multifunctional protein 1 (AIMP1), acyl-CoA synthetase medium-chain family member 3 (ACSM3), aspartylglucosaminidase (AGA), A kinase (PRKA) anchor protein 2 (AKAP2), A kinase (PRKA) anchor protein 10 (AKAP10), ATPase, H+ transporting, lysosomal 9 kDa, V0 subunit e1 (ATP6VOE1), adenosine kinase (ADK), ankyrin repeat and MYND domain containing (ANKMY1), adenosylhomocysteinase-like 1 (AHCYL1), adenosylhomocysteinase-like 2 (AHCYL2), adenosine deaminase-like (ADAL), ceroid-lipofuscinosis, neuronal 5 (CLN5), chromosome 20 open reading frame 27 (C20ORF27), chromosome 8 open reading frame 74 (C8ORF74), CDK5 regulatory subunit associated protein 1-like 1 (CDKAL1), centromere protein H (CENPH), delta-like 1 (Drosophila) (DLL1), dual specificity tyrosine-(Y)-phosphorylation regulated kinase 2 (ECHDC1), ERG, fibulin 5 (FBLN5), farnesyltransferase, CAAX box, alpha (FNTA), family with sequence similarity 63, member B (FAM63B), fumarate hydratase (FH), flotillin 2 (FLOT2), GDP dissociation inhibitor 2 (GDI2), glycogen synthase kinase 3 beta (GSK3B), G2/M-phase specific E3 ubiquitin protein ligase (G2E3), histone deacetylase 2 (HDAC2), heterogeneous nuclear ribonucleoprotein A0 (HNRNPA0), 2-hydroxyacyl-CoA lyase 1 (HACL1), IGHG1, kelch repeat and BTB (POZ) domain containing 2 (KBTBD2) GTPase, IMAP family member 7 (GIMAP7), GTPase, IMAP family member 4 (GIMAP4), interferon, gamma (IFNG), Integrin beta-1-binding protein 1 (ITGB1BP1), low density lipoprotein receptor adaptor protein 1 (LDLRAP1), leptin receptor (LEPR), lysophosphatidic acid receptor 1 (LPAR1), leucine rich repeat neuronal 3 (LRRN3), membrane associated guanylate kinase, WW and PDZ domain containing 3 (MAGI3), mitogen-activated protein kinase 5 (MAP2K5), mediator complex subunit 28 (MED28), myelin basic protein (MBP), mitochondrial ribosomal protein S14 (MRPS14), mitochondrial transcription (MTERF4), nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor (NR3C1), NEDD4 binding protein 2-like 2 (N4BP2L2), NIMA-related kinase 9 (NEK9), oligodendrocyte transcription factor 1 (OLIG1), proteasome subunit beta 4 (PSMB4), protein kinase C, beta (PRKCB), PIK3C3 phosphatidylinositol 3-kinase, catalytic subunit type 3, (PIK3CA), protein kinase, cAMP-dependent, regulatory, type II, beta (PRKAR2B), protein kinase, cAMP-dependent, regulatory, type I, alpha (PRKAR1A), phosphoglycerate kinase 1 (PGK1), plakophilin 4 (PKP4), protein kinase C, iota (PRKCI), protocadherin 9 (PCDH9), PITH (C-terminal proteasome-interacting domain of thioredoxin-like) domain containing 1 (PITHD1), platelet-activating factor acetylhydrolase 1b, catalytic subunit 2 (30 kDa (PAFAH1B2), polymerase (RNA) II (DNA directed) polypeptide D (POLR2D), ribulose-5-phosphate-3-epimerase (RPE), RNA binding motif (RNP1, RRM) protein 3 (RBM3), regulating synaptic membrane exocytosis 3 (RIMS3), RNA polymerase II associated protein 3 (RPAP3), spindle and kinetochore associated complex subunit 2 (SKA2), SUMO1 activating enzyme subunit 1 (SAE1,) SECIS binding protein 2-like (SECISBP2L), sorting nexin 6 (SNX6), SR-related CTD-associated factor 11 (SCAF11), SCMR8, spectrin, beta, non-erythrocytic 1 (SPTBN1), serine racemase (SRR), SET nuclear proto-oncogene///SET pseudogene 4///SET-like protein (SET), sulfatase modifying factor 2 (SUMF2), tumor protein D52 (TPD52), tau tubulin kinase 1 (TTBK1), transmembrane protein 245 (TMEM245), TMEM254, (TMEM254), tubulin, gamma complex associated protein 3 (TUBGCP3), TNF receptor-associated factor 3 (TRAF3), tripartite motif containing 23 (TRIM23), ubiquinol-cytochrome c reductase complex assembly factor 1 (UQCC1), vesicle (multivesicular body) trafficking 1 (VTA1), vesicle-associated membrane protein 3 (VAMP3), vasoactive intestinal peptide (VIP), tryptophanyl-tRNA synthetase (WARS), WAS/WASL interacting protein family, member 3 (WIPF3), WNK lysine deficient protein kinase 1 (WNK1), WWP2 WW [MMK1]domain containing E3 ubiquitin protein ligase 2 (WWP2), pre-B lymphocyte 3 (VPREB3), X-ray repair complementing defective repair in Chinese hamster cells 5 (double-strand-break rejoining) (XRCC5), zinc finger protein 565 (ZNF565), zinc finger, FYVE domain containing 21 (ZFYVE21), and zinc finger protein 75D (ZNF75D), identifying a subject as having suicidality risk wherein the expression level of the blood biomarkers in the first panel is increased relative to a reference expression level, or, wherein the expression level of the blood biomarkers in the second panel is decreased relative to a reference expression level; and, administering to the subject identified as having suicidality a drug to treat the suicidality.
 2. The method of claim 1, wherein the biomarkers are quantified in samples taken on two or more occasions from the individual.
 3. The method of claim 1, wherein the biological sample is selected from the group consisting of; a tissue or a bodily fluid, cerebrospinal fluid, whole blood, blood serum, plasma, and saliva, or an extract of the sample.
 4. The method of claim 1, further including the step of treating the subject with at least one therapeutic agent selected from the group consisting of: dissociatives, mood stabilizers; antipsychotics; antidepressants; omega-3 fatty acids; and anxiolytics.
 5. The method of claim 1, further including the step of treating: a subject who exhibits changes in ACP1, BCL2, CRYAB, GSK3B, HDAC2, HTR2A, ITGB1BP1, MBP, NR3C1, PIK3R1, PRKAR2B, PRKCB, and SLC6A4 with a mood stabilizer; a subject who exhibits changes in ACP1, AKAPI13, BCL2, CD164, CD47, CLTA, CRYAB, DYRK2, HTR2A, IFNG, IL6, LPAR1, MAGI3, MBP, NR3CI1, PGK1, PRKAR2B, SOD2, and TPH2 with an antipsychotic; a subject who exhibits changes in ACP1, CD47, ACP1, GATM, LPAR1, MBP, MRPS14, and SLC6A4, with omega-3 fatty acids; a subject who exhibits changes in ACP1, CYP4V2, NR3C1, PER1, PIK3C3, PSME4, SLC6A4, and SOD2, are treated with an antidepressant; a subject who exhibits changes in GIMAP4, PER1, and PSMB4 with an anxiolytics; and a subject who exhibits changes in one or more of ACP1, PIK3R1, SLC6A4, and TPH2 with CBT.
 6. The method of claim 1, further including the step of: treating a subject who exhibits changes in ACP1, CD47, ACPI, GATM, LPAR1, MBP, MRPS14, and SLC6A4, with omega-3 fatty acids.
 7. The method of claim 1, further including the step: of treating the subject with at least one therapeutic selected from the group consisting of: chlorogenic acid, ebselen, metformin, piracetam, oxybuprocaine, sertaconazole, fenbufen, alprostadil, tolmetin, tenoxicam, merbromin, adiphenine, ozagrel, procainamide, asiaticoside, carbimazole, ramifenazone, dl-alpha tocopherol, diphenhydramine, betulin, calcium folinate, dapsone, clemastine, dihydroergocristine, amoxapine, lisuride, homatropine, ritodrine, merbromin, naproxen, chlorpromazine, genistein, fluoxetine, yohimbine, prazosin, amitriptyline, trimethoprim, ethoxyquin, haloperidol, terazosin, pepstatin, diethylstilbestrol, nifenazone, metrizamide, baclofen, Daunorubicin hydrochloride, BRD-K06666320, WZ-3105, Piretanide, Syk Inhibitor, vorinostat, DACTINOMYCIN, trichostatin A, Tiotidine, and troglitazone. 